{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "classification.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU",
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "6cbe84c6aa4a4f34ab24bf0b97269ada": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "state": {
            "_view_name": "HBoxView",
            "_dom_classes": [],
            "_model_name": "HBoxModel",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "box_style": "",
            "layout": "IPY_MODEL_6c650fe9843c40958da8d3da0ab7c543",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_3f428a46df034a1485fffc8adc37425b",
              "IPY_MODEL_a33b6772a9604b0ea75b359a830609de"
            ]
          }
        },
        "6c650fe9843c40958da8d3da0ab7c543": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "3f428a46df034a1485fffc8adc37425b": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "IntProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_c78c009e46864818b07aee30ab5d4ee7",
            "_dom_classes": [],
            "description": "100%",
            "_model_name": "IntProgressModel",
            "bar_style": "success",
            "max": 102502400,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 102502400,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_972a61021325415388ce58cf16f56aeb"
          }
        },
        "a33b6772a9604b0ea75b359a830609de": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_720654d38cb14b38bc25b2e7f7da9031",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 97.8M/97.8M [00:01&lt;00:00, 100MB/s]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_ea24b57aae1c4877870a56e8e027abe0"
          }
        },
        "c78c009e46864818b07aee30ab5d4ee7": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "initial",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "bar_color": null,
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "972a61021325415388ce58cf16f56aeb": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "720654d38cb14b38bc25b2e7f7da9031": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "ea24b57aae1c4877870a56e8e027abe0": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dTYEp1mDKYwH",
        "colab_type": "text"
      },
      "source": [
        "#首先与我的云端硬盘进行挂载"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E8ZZbFb7Ki2s",
        "colab_type": "code",
        "outputId": "ec52f5e5-52b4-42c8-b0f1-6f2204b7647d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4H8m1x-DLldV",
        "colab_type": "text"
      },
      "source": [
        "可以使用ls命来查看云端硬盘目录目录"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LlYtJW1sLks0",
        "colab_type": "code",
        "outputId": "8039102a-7110-4340-a008-e8e5814acfc9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "!ls /content/drive/My\\ Drive/graduation/dataset/"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "test  train\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G_XBoopxK8ZX",
        "colab_type": "text"
      },
      "source": [
        "#加载所需的模块，进行数据的加载与预处理"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "COp_fftMLJHK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%matplotlib inline\n",
        "import time\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import datetime as dt\n",
        "import matplotlib.pyplot as plt\n",
        "from mpl_toolkits.axes_grid1 import ImageGrid\n",
        "from os import listdir, makedirs, getcwd, remove\n",
        "from os.path import isfile, join, abspath, exists, isdir, expanduser\n",
        "from PIL import Image\n",
        "import torch\n",
        "from torch.optim import lr_scheduler\n",
        "from torch.autograd import Variable\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "import torchvision\n",
        "from torchvision import transforms, datasets, models"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l8h0HzcpmPJ2",
        "colab_type": "code",
        "outputId": "b2da5077-7c53-4791-d42a-a2cce23f9bbd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(torch.__version__)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.4.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lr1L7KciMnAZ",
        "colab_type": "text"
      },
      "source": [
        "读取.csv文件中的信息,并查看"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1-E2RPYTLe45",
        "colab_type": "code",
        "outputId": "d1daeaee-b498-4b86-d2f0-1ba6a20cdcb8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 418
        }
      },
      "source": [
        "INPUT_SIZE = 224\n",
        "NUM_CLASSES = 16\n",
        "data_dir = '/content/drive/My Drive/graduation/dataset/'\n",
        "labels = pd.read_csv(join(data_dir, 'train/train.csv'))\n",
        "print(len(listdir(join(data_dir, 'train/images'))),len(labels))#输出训练集数据大小\n",
        "labels"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "14512 14502\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>image_path</th>\n",
              "      <th>labels</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>images/00002783_000.png</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>images/00017926_005.png</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>images/00002878_010.png</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>images/00000805_001.png</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>images/00001718_012.png</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14497</th>\n",
              "      <td>images/00019036_000.png</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14498</th>\n",
              "      <td>images/00002616_000.png</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14499</th>\n",
              "      <td>images/00016806_001.png</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14500</th>\n",
              "      <td>images/00001704_000.png</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14501</th>\n",
              "      <td>images/00003528_051.png</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>14502 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                    image_path  labels\n",
              "0      images/00002783_000.png       0\n",
              "1      images/00017926_005.png       2\n",
              "2      images/00002878_010.png       0\n",
              "3      images/00000805_001.png       0\n",
              "4      images/00001718_012.png       3\n",
              "...                        ...     ...\n",
              "14497  images/00019036_000.png       3\n",
              "14498  images/00002616_000.png       0\n",
              "14499  images/00016806_001.png       1\n",
              "14500  images/00001704_000.png       0\n",
              "14501  images/00003528_051.png       3\n",
              "\n",
              "[14502 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lBPNdeQzvg_c",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#labels_test = pd.read_csv(join(data_dir, 'test/test.csv'))\n",
        "#print(len(listdir(join(data_dir, 'test/images'))),len(labels_test))#输出测试集数据大小\n",
        "#labels_test"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5-8TVRLmnqHq",
        "colab_type": "code",
        "outputId": "3d08c086-a9cd-418d-9937-669e3419e913",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "selected_labels_list = list(labels.groupby('labels').count().sort_values(by='image_path', ascending=False).head(NUM_CLASSES).index)\n",
        "selected_labels_list"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0, 3, 1, 2]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XJMY6-Fgk1tZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "labels = labels[labels['labels'].isin(selected_labels_list)]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sjucCOxXk1tb",
        "colab_type": "code",
        "outputId": "f1c21b9a-2a92-45e0-bc07-4517655c82bd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "labels['target'] = 1\n",
        "labels"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>image_path</th>\n",
              "      <th>labels</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>images/00002783_000.png</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>images/00017926_005.png</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>images/00002878_010.png</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>images/00000805_001.png</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>images/00001718_012.png</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14497</th>\n",
              "      <td>images/00019036_000.png</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14498</th>\n",
              "      <td>images/00002616_000.png</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14499</th>\n",
              "      <td>images/00016806_001.png</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14500</th>\n",
              "      <td>images/00001704_000.png</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14501</th>\n",
              "      <td>images/00003528_051.png</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>14502 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                    image_path  labels  target\n",
              "0      images/00002783_000.png       0       1\n",
              "1      images/00017926_005.png       2       1\n",
              "2      images/00002878_010.png       0       1\n",
              "3      images/00000805_001.png       0       1\n",
              "4      images/00001718_012.png       3       1\n",
              "...                        ...     ...     ...\n",
              "14497  images/00019036_000.png       3       1\n",
              "14498  images/00002616_000.png       0       1\n",
              "14499  images/00016806_001.png       1       1\n",
              "14500  images/00001704_000.png       0       1\n",
              "14501  images/00003528_051.png       3       1\n",
              "\n",
              "[14502 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xp77jmAioQ7-",
        "colab_type": "code",
        "outputId": "17c1ab1d-a2b1-450b-946c-1f2ab79e85c8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        }
      },
      "source": [
        "labels_pivot = labels.pivot('image_path', 'labels', 'target').reset_index().fillna(0)\n",
        "labels.pivot"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method DataFrame.pivot of                     image_path  labels  target\n",
              "0      images/00002783_000.png       0       1\n",
              "1      images/00017926_005.png       2       1\n",
              "2      images/00002878_010.png       0       1\n",
              "3      images/00000805_001.png       0       1\n",
              "4      images/00001718_012.png       3       1\n",
              "...                        ...     ...     ...\n",
              "14497  images/00019036_000.png       3       1\n",
              "14498  images/00002616_000.png       0       1\n",
              "14499  images/00016806_001.png       1       1\n",
              "14500  images/00001704_000.png       0       1\n",
              "14501  images/00003528_051.png       3       1\n",
              "\n",
              "[14502 rows x 3 columns]>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6qtoIpQ_ol4-",
        "colab_type": "text"
      },
      "source": [
        "划分训练集与验证集"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DptHu8Eloi8e",
        "colab_type": "code",
        "outputId": "ad497b00-1d97-4db7-b00d-2f135b6341d7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "train = labels_pivot.sample(frac=0.8)\n",
        "train"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>labels</th>\n",
              "      <th>image_path</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>14365</th>\n",
              "      <td>images/00029551_002.png</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9591</th>\n",
              "      <td>images/00013849_005.png</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1444</th>\n",
              "      <td>images/00000927_003.png</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4184</th>\n",
              "      <td>images/00002509_006.png</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8013</th>\n",
              "      <td>images/00009925_047.png</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1023</th>\n",
              "      <td>images/00000633_001.png</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6098</th>\n",
              "      <td>images/00003923_017.png</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>812</th>\n",
              "      <td>images/00000520_000.png</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13289</th>\n",
              "      <td>images/00022826_007.png</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3101</th>\n",
              "      <td>images/00001901_005.png</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>11602 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "labels               image_path    0    1    2    3\n",
              "14365   images/00029551_002.png  0.0  0.0  1.0  0.0\n",
              "9591    images/00013849_005.png  0.0  0.0  1.0  0.0\n",
              "1444    images/00000927_003.png  1.0  0.0  0.0  0.0\n",
              "4184    images/00002509_006.png  1.0  0.0  0.0  0.0\n",
              "8013    images/00009925_047.png  0.0  0.0  1.0  0.0\n",
              "...                         ...  ...  ...  ...  ...\n",
              "1023    images/00000633_001.png  1.0  0.0  0.0  0.0\n",
              "6098    images/00003923_017.png  0.0  0.0  0.0  1.0\n",
              "812     images/00000520_000.png  1.0  0.0  0.0  0.0\n",
              "13289   images/00022826_007.png  0.0  0.0  1.0  0.0\n",
              "3101    images/00001901_005.png  1.0  0.0  0.0  0.0\n",
              "\n",
              "[11602 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gShTZnE1qKf-",
        "colab_type": "code",
        "outputId": "526aaa6c-7b89-4d1b-c16a-88436713af47",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "valid = labels_pivot[~labels_pivot['image_path'].isin(train['image_path'])]\n",
        "valid"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>labels</th>\n",
              "      <th>image_path</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>images/00000005_000.png</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>images/00000005_006.png</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>images/00000010_000.png</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>images/00000013_000.png</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>images/00000017_001.png</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14471</th>\n",
              "      <td>images/00030408_015.png</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14480</th>\n",
              "      <td>images/00030550_001.png</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14486</th>\n",
              "      <td>images/00030606_002.png</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14488</th>\n",
              "      <td>images/00030609_014.png</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14489</th>\n",
              "      <td>images/00030636_002.png</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2900 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "labels               image_path    0    1    2    3\n",
              "1       images/00000005_000.png  1.0  0.0  0.0  0.0\n",
              "4       images/00000005_006.png  0.0  0.0  0.0  1.0\n",
              "5       images/00000010_000.png  0.0  0.0  0.0  1.0\n",
              "12      images/00000013_000.png  1.0  0.0  0.0  0.0\n",
              "20      images/00000017_001.png  1.0  0.0  0.0  0.0\n",
              "...                         ...  ...  ...  ...  ...\n",
              "14471   images/00030408_015.png  0.0  0.0  1.0  0.0\n",
              "14480   images/00030550_001.png  0.0  0.0  1.0  0.0\n",
              "14486   images/00030606_002.png  0.0  1.0  0.0  0.0\n",
              "14488   images/00030609_014.png  0.0  0.0  1.0  0.0\n",
              "14489   images/00030636_002.png  0.0  1.0  0.0  0.0\n",
              "\n",
              "[2900 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c07KS-vEqWG2",
        "colab_type": "code",
        "outputId": "f0e4436c-91c9-4ab9-d288-267d84a09239",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "\n",
        "print(train.shape, valid.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(11602, 5) (2900, 5)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aTjhnKT42cwJ",
        "colab_type": "text"
      },
      "source": [
        "数据集预处理与加载"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GU-HZmxrk1tu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class ClassificationDataset(Dataset):\n",
        "    def __init__(self, labels, root_dir, subset=False, transform=None):\n",
        "        self.labels = labels\n",
        "        self.root_dir = root_dir\n",
        "        self.transform = transform\n",
        "    \n",
        "    def __len__(self):\n",
        "        return len(self.labels)\n",
        "    \n",
        "    def __getitem__(self, idx):\n",
        "        img_name = '{}'.format(self.labels.iloc[idx, 0])\n",
        "        fullname = join(self.root_dir, img_name)\n",
        "        image = Image.open(fullname).convert('RGB')\n",
        "        labels = self.labels.iloc[idx, 1:].as_matrix().astype('float')\n",
        "        labels = np.argmax(labels)\n",
        "        if self.transform:\n",
        "            image = self.transform(image)\n",
        "        return [image, labels]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QsBsiKLjk1tx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "normalize = transforms.Normalize(\n",
        "   mean=[0.485, 0.456, 0.406],\n",
        "   std=[0.229, 0.224, 0.225]\n",
        ")\n",
        "ds_trans = transforms.Compose([transforms.Resize(224),\n",
        "                               transforms.CenterCrop(224),\n",
        "                               transforms.ToTensor(),\n",
        "                               #transforms.Lambda(lambda x: x.repeat(3,1,1)),\n",
        "                               normalize])\n",
        "train_ds = ClassificationDataset(train, data_dir+'train/', transform=ds_trans)\n",
        "valid_ds = ClassificationDataset(valid, data_dir+'train/', transform=ds_trans)\n",
        "\n",
        "train_dl = DataLoader(train_ds, batch_size=4, shuffle=True, num_workers=4)\n",
        "valid_dl = DataLoader(valid_ds, batch_size=4, shuffle=True, num_workers=4)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iKcbfnkEBETM",
        "colab_type": "text"
      },
      "source": [
        "查看图片"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hXwZ0Qxak1tz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def imshow(axis, inp):\n",
        "    \"\"\"Denormalize and show\"\"\"\n",
        "    inp = inp.numpy().transpose((1, 2, 0))\n",
        "    mean = np.array([0.485, 0.456, 0.406])\n",
        "    std = np.array([0.229, 0.224, 0.225])\n",
        "    inp = std * inp + mean\n",
        "    axis.imshow(inp)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wryyPfVak1t1",
        "colab_type": "code",
        "outputId": "67073ab7-986c-47c6-cea2-0831cee69bfc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        }
      },
      "source": [
        "img, label = next(iter(train_dl))\n",
        "print(type(train_dl))\n",
        "print(img.size(), label.size())\n",
        "fig = plt.figure(1, figsize=(16, 4))\n",
        "#img = img/255\n",
        "grid = ImageGrid(fig, 111, nrows_ncols=(1, 4), axes_pad=0.05)    \n",
        "for i in range(img.size()[0]):\n",
        "    ax = grid[i]\n",
        "    imshow(ax, img[i])\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
            "  \n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "<class 'torch.utils.data.dataloader.DataLoader'>\n",
            "torch.Size([4, 3, 224, 224]) torch.Size([4])\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5kAAAD8CAYAAAD9h2BSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjAsIGh0\ndHA6Ly9tYXRwbG90bGliLm9yZy8GearUAAAgAElEQVR4nOy9SYis25bft75oMjMisj3n3HPffQ1V\nD+EaCcoGyR4IjYTBM+OZy2APDC5PNDB4YjQyaOoGjwxlbITBYIHskREURhNREyOVMbhRNVKp6tV9\nt3mnyZNNZGRGRsTnQd7fjt+3ckfmufeec+9578WGILrv299uVvdfa+29m7ZtY1u2ZVu2ZVu2ZVu2\nZVu2ZVu2ZVu25V2U3vfdgG3Zlm3Zlm3Zlm3Zlm3Zlm3Zlm351SlbkLkt27It27It27It27It27It\n27It76xsQea2bMu2bMu2bMu2bMu2bMu2bMu2vLOyBZnbsi3bsi3bsi3bsi3bsi3bsi3b8s7KFmRu\ny7Zsy7Zsy7Zsy7Zsy7Zsy7ZsyzsrW5C5LduyLduyLduyLduyLduyLduyLe+svDeQ2TTNv9U0zR83\nTfPPm6b5z97Xc7ZlW7ZlW7ZlW7ZlW7ZlW7ZlW7blwynN+zgns2mafkT8SUT8mxHxaUT8k4j4nbZt\n/793/rBt2ZZt2ZZt2ZZt2ZZt2ZZt2ZZt+WDK+4pk/usR8c/btv2ztm3nEfE/R8S//Z6etS3bsi3b\nsi3bsi3bsi3bsi3bsi0fSBm8p3p/FBF/qe+fRsS/senipmna9P29NOrr1PvQtd/0v4iItm2j3+/H\nzs5ODAaDcCSZe/mtaZro9XrR6/XKb/1+v3NP27bRNE3nxb2uJ7/n//ju+h6Lcuf/XUftv/za9F+t\n7qZpYj6fx+3t7cbn59/eR5T+l7y8bNv2o4iIo6Oj9vnz53FzcxNXV1dxc3MTy+UylstlrFarezdm\nOvm+y2Pt+SYypG3b6PV6MRgMCt/1+/3o9/sdXoQ/8vfMg25Hfvd9tfbCh5mner37fkHfW+Mr5rP2\nnz/zfbVade71d/++XC5jsVjEYrGI5XL53uT21y2MW03OvY9nbfqtbdu4vr4uPDcYDNrhcBjL5bIq\nIynfJ4+VJ3+NNmzSP/7/m7z7M7wI3/n9bZ/70O/Ul/uTS+YbZEC+ZtP32ue3vd7Pz/f6HX59qNTu\n2yQPam3iGeiLh3TGpu9f97q3rWM6nRaem0wm7WAwiNlstlG3bcv9MhgMYmdnp+i/fr9faH2Tztv0\nqulMiunMcpNX5tFsL7vefK/pNPPFJh7K+g0av7m5ifl8HovFovP82ufa91p57BrrsW9T19s856Hf\nNv3f7/djOBxGr9eLL774ovBcLu8LZD5amqb53Yj4Xb4Ph8MOsZgQH1McDwmetxngrFQ2ATeIEabJ\n7XmI0Pi8Wq1id3c3fuM3fiOePXsW/X6/EPRgMCgCG2Y8Pj6OwWAQ8/k8+v1+HB0dxeXlZfT7/Vgu\nl3F9fV2M4r29vdjb2ysg9vb2trQVhun3+zEYDO4ZzqvVKhaLRSHs0WgUy+Uy5vN56XPN8HTfb29v\nY7FYlLoYs9vb27i5uYnb29tYLpfld79ub29jPp8Xpvb99OFnP/tZfPbZZ50x3mRUU08WNDUFenfv\ng2QS35X9vPk5+Q/oLt+/uaGLxWLaNM0/jYh49uxZ/M7v/E78wR/8QfzRH/1RXF1dxfX19b1xYeyz\ncH/MePf/tWsf+/+x8pgzIYOM2r2bjLzlchmj0Sj29/djMpnE7u5uDIfDGI1Gsbu7G7u7uzEej2Nv\nb684i3Z3d8t3K2V4rdfrxXA4LNceHh6W+iKi8GCv14vd3d1C//D5dDqN6XQah4eHsbOzU/oGnbvt\nGFKLxSKurq5iOp0W3gQQwot8x4Ezn89jPp/Hzc1NzGazmM1mcX19HdfX13FzcxOLxaLw83w+j+l0\nGpeXl/fGuCsPm++Mf77L0qWbGiBZRUQUntvZ2Ym/8lf+SlxeXhaDJQPO9u7GaIF75a2N9NNDDVvf\nU/1/Ux/Wz7/3nAofoUug6+FweA8I8hkdVTNauQfDpWbIDgaDODw8LPw4Ho9jNBp1eDAbwQ8ZxVxP\nP/r9fkwmk9jZ2Snt5TM6aLFYxGw2K3wyGAzi448/jv39/fI7TlD6hW6HL+FXrs06C93KPVlf+V4+\nw9eLxSKur6/j6uoqkcN67rLTiHZZFqxWq/KM29vbcg9yxdcjH66ursoYRNy3i2qOgPzbpms32Xo1\nx8ByuYw/+ZM/KTy3u7sbk8kkLi4uvlfnzS9bGQwGMR6PYzKZxMHBQdGF8NvOzk4Mh8N7L37nmt3d\n3Y4uhX+hv8ViETc3N4W2xuNx9Hq94vhGz+3t7RV9FhHF1kWfjkajYj9Dj+fn5zGdTuP6+rroMus9\n+M/O0sViEfP5PK6uruLNmzdxfn4es9ksvvzyy7i4uPg+p6RTMibJ3/N13+YZm+ofDAZxdHQU4/E4\nIuIvNtXzvkDmzyPiJ/r+469+K6Vt29+LiN+LiOj1eq1BZk0YUTah6re5dpMXJNeR694UPfD19qT4\nvlq9/X6/CHD6TR1+Hky6Wq06XgPA4u3tbVHQADV7ijA+HQkFAGIYrFaraJqmKB7A4nA4jNlsFjc3\nN8WYsPGa+2sFiZKCeV03kdga2DPQ7ff7pV4b4Ds7O1Wvq/tYa09+ZfCZpz7ro2+qn74uj9eec1dH\nNmjpf9O57hHP18u2bf9aRMSzZ8/af/AP/kH8+Z//ecxmsw1tb2INZr+ep+5tePZt63qsfB0v3hp8\nrscrF5w3y+UyLi4uOsY0yhMD1woWAIqxzX0YnNQxHo/j4OCgKEaKgWnOXpjNZjGdTuPVq1cd4xel\niFFsY/H29jZms1mcn58XpWplauWK4cw7wBLAeXNzE9fX18XotEH6rufsl6Hczc1beaILz00mk7bm\n4Hr0WeL9jXfeA4sPA8wquKx9fwRcZqeK9apBZc4MgMZ9LXI/62VoHdrbJNvRg0TVuS7rYv6zA3Yw\nGBS+j4ji7DFYvb6+Lg6bpmni6OgoIiJms1kMBoNi0Jq2HRGBb1arVckc4XfmpAb4/L+dQjgq7Pxx\n+7rkcT/CY10IqDXoZMzdFutNA3aAQ9M0xaFsXbSJ3zcBTv9f+54d3Hz+aqwKz43H43Y6nW4B5tcs\n8/k8Li8vC31dXV3F3t5ecYwOBoOiczLgzL+NRqMCWE9OTmIymcRqtYrpdFpsydVqVYIpTdMUOt7Z\n2Sngbmdnp9iH0+m0tIF2DYfDoqum02mcnZ3F5eVl0Z3T6TTm83mxT7PTB12Irru8vIyzs7O4uLiI\n6XT6Pc9It9wPkrz7rJjH7p3P5/HixYt48eLFg9e9L5D5TyLiX2ma5qdxBy7/3Yj49zZdbM/iJo8W\n1/n9ofpy+JzfEXwGsxaYjti5PrcFBVVr10OpBDkaOJ/PC8NmwR0RxZhEUe7s7JTPFrQAMj8LhbFY\nLAqwMwCFuVDqi8WiA+iurq6K0WkgigLy823wWgHm8XEbAcDUa8+vo1Cer4goij8rvfyMWuTSStuf\nM+CsgU4XAMrblscA1aaI2gMtoLbO5xpw2sQrV1dX8emnn24EmL+KZT3OEY/Fg6DP5XJZeNpKFSBm\nZbq7u1u+1yI3OIqIAgJSLUtub29jMBjEZDIpfLtcLmM2mxUwOR6POwYdvDYajUqbUbbz+TwuLi6K\nU8vgMitXrnE0k88YtDXj96HxvqO/tuoMcfllB51vU2o67Z2UxyKXd3/q8oeu25x+DjgEYELnNT2X\no5cGmfk3603rKNfndvu6GpDNwKWmk6mPfuGgiVgvScG5u1qtCn9HRIm8YJBGRBwdHZUMg4g1D25a\ngoCe3TQXm3QY7c5gFIBMNtDm6X1YL9Yirwbs1pnYDowfn/v9/oPphQaf1mS+5jGduQl41ozvXwfZ\n8q5LLdgTsXaamEc2ReGZE2giYh1gGY1GJTsAPdi2bVxdXUXTNMXOe/PmTXnWxcVFyWJA/rRtW/ix\n1+sVmxUnKZF2HB+8O+sAXjbIJMAyn8+Lw2Vbvll5LyCzbdtF0zR/OyJ+PyL6EfE/tG37/z50D0Iq\nKyvKQ8rZvxsA5giXQVxWNvkZ/o131wUDZcWWP2clGhEFuKGIUMYGkIyJBedwOCyeHArMS5thbgS+\nDcuspFEOvV6v3AdTkebXtm0xYrOyMQh0dCODZXz+OSJsJRYRHbDt5wwGg+IZRfjgEbby5nNWOLQl\nK9KHwGYtxXZNQw+ncGf6ekzJZZBZe66urnwGbDYdG9HG/K+7ou0CzPVvNeWRjVgApiOUGNi1tSo4\nXByRNE0D5Gqyg9Q/r3F02hwKFXDa7/djf3+/XG+gmI1Ht6HWd8sNR5zy9XYSbQKaNk4Bml895R6O\n+XWh00309i0rvXvLAPOBx9hBWKsrQjFagUvo3wCT+vwCiG4CmDWQST0GmRFrHWraNb/kCKrvq8nj\n2vO4/j7N3v1Of9E5GMfoXmfuOHLK+JjnPAfo/Nr85P89Z+ZRL3GJ6NoDtZJ1S9aB1uO8qNeZD54b\nZA9OOMYLOUSk1dFmdbbQWhuSA9lR+tVFXF3qaJpoNF+18dwChK9fzMeb1mM6pRyarDmJKPAJjhsv\n60Jf9Xq9GI1GxV7b3d2Nq6urshyF35fLZbGPuc+ZAjlog6wYDocRseYDgituo51PONEcANmWr1fe\n25rMtm3/YUT8w7e5FoKtgTP+z8ZHBoQQhg29DKpqv/NuBWPAV3t+DUTktmcFmNtIG1arVYmI4BmM\niMIQCE3qgTlrRlk2IPNzPVYR0VEmpNCyQH5/fz8iIkg1OTg46Ch7e6os4BmLjgexXZtAWdHWnAL2\n8qJMSZkgfZh7nCqVhcsmwzUDyAw4M5DO7c6lRh+1+2p11Az9bAh029O5O7ope12/sPTwvedQ569D\nuQ8wH1YUeY2Y1zHbwM5pgrWySU7YmIPmUaRt2xanCooZ7yy83zRNUfBkOEDLPMuGrmnZssDtyuCa\njcmyTPHLhmht3NeyICJH3tfXMVZvF4XflugAwg7A3JQlm3TAvcsrcggj07Tv75mu0XmbjFNHvjZF\nF7ORmnWDQZzfM3Csfa+BTa5D/2Wasx4me8FOH9Lp9/b2ig5xnz2emQdrQDK30cZtzXbJdddsg2wj\n8A5vey8IHFg56wdwiQPM/bTebNu2OCSQcY4meeyaO4bvpAVt5HjSvLVHZBPd+zfp6C0w+HoF3rfe\nq4HHbGNlusNWNT+SMYMciFingUesHaos2XKmHbYfz7aDBTrclDmQQTC6LfMGOp3fkWUsRduk67Zl\nc/neNv5xwWAy4IiIewSaI31ZyUFwNYBokGaFFhEdw8weQRNhrsf12ZNj5ZAVaAYwi8UiRqNRCduj\nxNu2LZEKmA4FiKCvGY0uKI/cZ/eD/jmlhXvNTDZO6BfpRfYe5esLGGzu/I8e34g7vWHgaqPCBm/b\ntmVtqPtjhennZ4VsmvBv+XtWrJuUelbUBob5+fn3XEcumQdc3/3Ia7WKSp2bfv/VVr7d8S6f7v2X\nC7ycFa0N6ByNyUAzG5k8M9Oq04+ok2vJKCAFzsofJw/37ezsRETcyyaAZ2vjYjlgIIChaCOU67OB\n/pijYt2O9djfjZPHv+sUubvmw0l1y3LrXZSqw+nuj/RD7XMlepmry0OsZ957TnQdfgaXXmuVaZT7\nrP8yr5g3rB9rEUveM4Cy/uFV47ma4bvplQGgHTToTaKYdnry2bLY/7s/m3Sr9Stj6Sio+dIGtIEg\nfGe9ZZ3uMfXn7GzIdVrP8Pza5kB2MmO/GGSSmbZarWIymZSNVwCcRJA6vMVcOrQZazq/A5Xrsayl\nh/+q67T3XZqmucfr5rVNvMu12e41SEWe7O7udr57rb+Xdll/8VtuD9FQZ1aQvVdzhKHX7Dy1znWm\nEjxB2ncOPGzL25UPAmRGRHVNZvY2OkoVcd8D72s2XWcFaRCTIwBZWOfPbp8BUq3tNfDh9U3klpOW\nmsP6jA+CnzTYGnixAqMdecc7Mxr3eh2Zx5x6aBdCqNfrlU0SuMaKz4psU+nE4L66zt5RBAqLtUmV\nNWi2orLyNfCzQMlAzzSSFW3t+q/zcr82gc1a3bVrmHccDFb237R8CAb8+yh1cBmxtlByZHNdkBG1\ndZU5gpmjnDVF63rzeGeaZ9Mg1mVGRCedBznF2kwKUYWIu0h/v98vO+RRl72wNZmZAW6mx8xvvJy+\nv9lx4vE1yI14KLrpa92G76pgMOPJfheR/8fkQESknWT12XSt3+8VDWn3ntKI7uUCVN4p1sbbJp3M\nbxlc2kGcHTH+XNMzNV5xqaWBr7vWBXo18AqdWz/jTHGkA8d3xFqvGoBZJmRwlttgnQUd5dS87ICy\nzVKLzKCzOusne73i0K3ZLNlhkoFkBp5Zf9acyqQ6ug7XPxqNYjKZxHw+j9lsFpeXlzGdTu9vEFSY\nnk6u02NLfab7r/5v2zbaaO9dy/N/VfXcuy62D52lU7PLs62beTzr0bxJD/IGejLAs7NrPB5H0zRx\nfn5eaGl3d7foQQI1e3t7nY20kGG1oEVus7N9aLOjouPxuMMLPkZvWx4vHwTIxMNgheD1DFkQ51ID\niZueY0FrAGdvfX6OPSJZWW3ynPraGmBB8SwWi86RBAjd7B0dDAad9AAf8WEh6jQWjCQfHcLYZm8l\nY2hjw+tObIBGrKPKXjvmcbUSzOOzWq066bP+30qZcnNz04myuB3ue80jy/9EenKfMz3UwJ0/e9wM\nqvNng1/alo2ITc97SDEyTzgd7KzQVemuh1M5H3QE/BLr5we6FRGAGD6vebYWsXSqrI3xDDofMqgp\n/MZ28OPxuDhQOEYGevE6MJQewBFA6eiI099Xq1Xx6tIWeMLKd/P43Qd4AEtHQDEWMjit1Y0dqV94\nwsOT9R0X+OK3f/u347d/+7fjH/2jfxQ/+9nP0jUP1/EY/W284QGAmcFldZwrALPWIGjbm1axo7md\nJcx1RBe8ZIPU0UsD0k1GaY0+zIeW9XbmIn9dR5bnmQfdZrKCst5zRMbrqX3cgUEY7cQhjPM3Z0t5\njTX8anuFcR4MBmX9osejBsg9/x1gmuaX+2u2SkR0+pHBZE2vrVarkp6PPs7HneRNg5qmKXLu4OAg\nDg8Py0690+m0bDKYo7leDVLmP5o7QMm8Ay7bemRzW96uQH/59TYO1bxsxK+sKyksD0PftW0b0+k0\nRqNROd6r3++XDfDYiTZizcPoM/YxyDSUwa9BpfneoJa2OIUccGt5ugWab18+CJAZEYUAszLKYCGi\nbpxnhZKvp2Qm4JXXhFoI1xRmrsvf3SZ+94Y8/GfDkBQFDDiDIZ5NXrjXcrlkxQAY8dpOX+ex9QJn\nvvNfzXDs9XoFHNuQrQHLe/XEne5Y+bvG0OPPzpYWDBFRnSuUntN5uH6TI8CCJD+b+2zIOC3Jipf7\nrKDdXu7JRlE2bmzYmWaz4jewwEOMgXIfNNaV7yZD78H/vy8s8DXsh7VBmu1qQjxNAQimB/qLErPy\nzMrSqbSeO0dDckSTZ1CXHT7sBsu8uz68qYeHh3FxcdE5rwv+ynOPYcfZee6j++3Iv+usea/zd8Bu\nDWBuzhzgGY/N4bot30fhuT/5yU/ir//1vx5/+Id/GJ9++umjTqDHSm1MOt/jPpisAUwb8Ln+2uc8\nj0QqOXbHR+88ptNyxCPLsJouNu35mpojI3/PsttyNTspfZ3b3rbtvfM0KT7SCz3oZ6CXHcXr9Xod\nQGg9naOO8LIBHSDN4I/PPi7BRjB1PMSXeeyQZzW7hXbb2M4gOvO2/3Nmk49IWi6XsVguY7W8vysn\nNDMej+P4+Lgcj3F5eVmOm8hZA+5LpgtHLzdF9x9yom7L2hEC8HOqvPVQDWA+9JsDN54DgCB17uzs\nxM3NTezs7JQjTpBNZBPs7+8XuoHu2E2d5zgKabsdHZrtXmxK2hixtgfJLsy0YxmUU+G3pV4+CJAJ\nMTiNxZO/yZNq4Zo9KTVFnpWPBXwWwBl0+D0/2+3cpCj93QSNcCbSRo45ishRDKKeeU2E+0m9/mzA\n6HpRMD5fE+WHssy719XmLQsQe579UkMj1JZI/9MWNjvht+whrilVCxXGLANq3+O+up4yT1yv+x09\n8rhlxZiNURtZeQMNj1ctTSmPu6NrbOuNd9ibU3zV+Htzl8et9vtd/zfe+t0W2vGIvfC4QdEFmBHd\nzbosizBIM6i0EjbIzEa35zZ7fqEFoo20G8Vqry+KeLVaxf7+fvH+0vamWUcXTTuOYFqelZEQD5pv\nLVvyeCIr2R0XZVyLgGT6ze/3J9Sf7wOMB6b0nReex1mjnUyBt3xeLX3PpQoIE8vWxmwTyH1IRkOf\nBpeOymf9xr38lnkC2rbOzhGDDIYyYNrUn6w73DfTbI02a3qZe3zEiJ/JubcRa91JpMJZPwAoeOTy\n8jL29vZid3c3bm9vyxEnWe8xTugk+N99y4CRMco7uZqXs+2R6/J1lkW+z7qa/luXZZvC424aQEZ5\nGQcyyBuxUAdju7OzU85PRH9dXl7G1dVVSaelPTUgXaKXbZQo57a8XYHeatHL/J552w6l/Nk6MyI6\ngRP0ne2mXq8XJycnBUjazuKINZ+5CW1j29kBlHdXt96F9yLinnyBB7gGu5lrsRHNDzk1flvq5YMA\nmRHdSCbvFrwQgI0rX5s9tBawy+Wys7NZNiiz8KJYEWzyzvq/XPy/v0fEPeUD0ARkRqwFPts8Q+Qc\nYwAz5k16YDi8Oh4TK6McbcgGCe01EKO9PD+fhZWVv5/h/ltpuA0Ggmx4gqKnjZsAvsfAwiX3t2ma\nTlqTlX42amnxSnSQ17ZlQWMFb+Gbj72AprMSzkaB6Svfg/ETsT5XdTAYJI/wYwjtQ0GSG8rXBJab\n5L7BZR7bGn9bgdZSYu3B9XU1B0iWMyjXiLVjxPc5So3T5OrqqvxnOrDhzj3IFLaMJ23etEnhNzIB\nLPP8W9u2RabM5/OOky47RjaBzfvAqkab5qk7p4Dvuydr72PTd1aqOoLUvK+Z6VsDTfn/t3mv3eOS\nQaHPcbVzKhuGWbZm2q+lwj7ES7X/GFNop9aHmlHnevJYUjbp8RqwyuPatm1x5PgeaNfnyzbNXVp7\nr9eLjz76KCaTSVxdXZWlC85siVhvRGLbw0eBMGeOECIThsNhXF1d3VuWQslRyjxeGVTXdKXHJDuq\nPD7WUejlXId19W/8xm/EcrmMf/Ev/kVn/PLc0VdSao+PjzvnHJKl42NiNhn3tfnflnWp8XdOjbUD\ndRPQzDqy9j0iOmni2Yan7tFoVFLYDQztLIc3sWu533aQnfz+7r5CewDJrO8yUEbnRcS9ZSE+J3db\nNpcPAmT2er3O0RRMMIt8I6KzuxOfs4HVNE05qBVixfD2Aa32DmZPLCWDDZ5TU5w1wWZglZWjC8SO\nMchmC2YQ1mrAJGwBzTNqihfvEfdErBl+kzFAXd78wWPL9TCaFafTN2tjkdOFa0rM7fRi8NpYZmO5\nNsa5/36Olak9t7X5zLRQA+puj8eR+eQeaNbC1+PsdtpYpzTNnSNlb2+vA4zZXh+hZ0EYUT8/7INW\nyG/hIOwClvo17uIm3s3KhTm0kyCDzvzbJkM9v2y03tzclDlAwZum2LxnOByWDRBsnOKxxQjIaW88\nEwMOUFqjX4+Fsx7MQziZoCWnJplPvWa5Bj6zsdoFm3eT37bQZ6u57QLOKv3mqt5B2SSnvskzXUet\nrpIuyzOjy/s1wJkNo7yBTzYUbXRlw3CTY4WX5WwGMg+BzAz0LMPpi2VUDSxlGZ9lWqYHaJ972GMg\n6wn4MSI612ewa/pumqasH2uaJvb392O1WsXV1VUn5daOTOu3bARHrNd5EcHEwGXjkexM5j0bzDXZ\nZPljsFvTw6Yz/56zEgwyc139fj/+1t/6WzEej+Pv/b2/F+fn5xERxTlVS1+0vNrd3Y39/f0SneK8\nzZubm7i+vi7LaHI99/iprUf9f91KTedt4vGcsbMpkpnHNfO9S6Yr6MU2pe+1XeX0V/SX6dsOW9uk\nNbsRGyvXk3Uez81g07Q2Ho8r+2FsSy4fBMhsmqacD8cE7+/vx2QyKQDRwNBeBNK2IqIQAukrEDS/\nnZycxHK5LCkZ2ZNokOmSjRoEe9PrFevW/1nQoVy8ZjEDQ5TEfD4vC4ypA4VFGN/A+qE2ZsbEQMUo\nzYYvdeLtbts29vb2YjqdVoWJx87ttcDIIDzPYW3cHKEl+uJrEXoZeGVjx8ol15uFEP/n9Ieacem1\nQI5oMo9ZSLtf0KeNPWjDu4FC7zbKa+OF0DSY3d3dLc6ZwWAQFxcXnVTKXDK9fm/la4LK9W/1a2vg\ncpOTqGaUcV8NjNYMuKx0a9fk9uBMsaPLKUar1ao4DnZ2dkpkP6/NZsMWeIUUVj8PkEkWhHmQsbWy\ndZ8tlyOiI3NrRjj3ZPqtfa8ZuRGAyxy9XAPOTLcdGfU1I4yPlcFgUPRRRMRsNru/HuctgeYmQ7j8\npr8KwGzWv2daNKisnd+a6dW06utq0fhN6XCWQfl9E8B02yPu73uA/DR4NKis3Zf1jkuv1ysyEV0A\nn1kPWF+iox3txUmNs9u65+joKJqmidlsFgcHB50NhSK6O0ObJzLwQRaYzxz56ff7MRqNijPatoFl\nTAbm0EeWT3lOvIQgA0nTqMEsz8/1+T8ik6PRqKw3R/95kzLLhQw8iWB5+YtTIgGe8/m8/JbLB6Hf\nvqfykJ5zxNw8n6OadjBRZ03XPcTzNT2aM+Yi1nYrgM4Azna0Mx9pewaOD8mhmv7O3+30Rx5km/H2\n9racJb8t9fLBgEwimRF365IODw87XgrSPRHQrCtBkHv9Xg69c89gMIiDg4N4/vx53NzcxMXFRTke\ngzq8hqCmDMwoEXG3ZbiAmpnChtlDREidGJXeOcuLlgFd+d6sXHiegSmgBy8gTITQNrObkXIkzNHQ\nGihziip951kAaafZ1Z6Jos+RDytMK2+Pv8fS8+A2+uV5cr1ZmNB305MFmT1/LjYGqRdaRTlmmvPc\nOsKVX8wd6xYYI4yr4+PjGA6H8ebNm2paxwcjGB9oxn2DvHaTeZT3+0qupnj8u1OUefZDSiuXrKyy\nkyVibfxaSWLMkslAWhh8S3zd+jwAACAASURBVDrRfD4vxu5sNrtHr/C3PbuUnFptfvE45+8GnuZJ\n0uIeik4+BDRr/2W+rDl8aOP6+wPRzW8BNqlvNpvFeDyOv/k3/2b81m/9VkRE/ON//I/jL/7iL74R\n/7xNH9u27aTjNk0TvX53rRM8Di3lbJNN9F972ZDMANUGYdZ9LjWDzte5PZaf8JjHAPlde06tPtMs\n9ZrH3Cb6kR0cdhjauLR8d8pqxB1vkMaJI8jpotRrGe8xyP2xjshOmKa5c8QTLXWUxTLK7c5zU5Nh\nmRYBnXf33W+j21QDme4L8gK5ZrDirLQa4HT/a/LCoNPRzqurqzg9Pd1IN78OhbnY5GDaBDKzHbPJ\nYeXnbJIXpi8AWk7HzcCVOW6au82v0H9ErE0/tX1GaA+86nbZLsPp7leOZFo+5Qwz88t4PC6098HY\nUx9Y+aBAJgYQRhWTtru72xEyJm7ypb3uIRuMGOR4oEejUdmtETBqQUVarReeO80sK/IsCP2fvbIZ\nUGXBD3CbzWYFaAKCqQtPHfd3lHTTTafyRgO0H2OV9hrg0FfSc6bTaQGkVrDUmQ0lG6YZrDEnPNPX\nUKwcPXcR3R0J7VGFTgy+bRzY+OT5NSPD8+o59RwypnZA5A18qAdPHPRluvJaU6+X9VEzHjvamJVu\nHnvmkI1hiHAdHR3F+fl5Z9fC77V8DVB599sjN31VmqZu7JrPah5VG6CZVg00fU/3ud10nFynadoe\nY35zIYI5GNydKcb1KN35fF5kXq/XK1Fr0wSGVzY2UcA2gvPzcTxt6l8eD788f9Aj92wCl5uiGFmZ\n18DY3TOdWrshPe4bgE3q+aM/+qP4/d///Tg+Pi7HzDwEfh6r87H+lb5FE01vnU4PL/vsyloK63pc\n7jtFrb9ytAKazHK25uHPz/H3Gk3wn+fNbTJN0L4aCKIUB2/l+QAaZ5Fk2kDe5gwR5LmNY+u0PK52\nsqIb+JyBM4VrsgxgPNDHGfgxDmxexMY40IcBbM5G8Ph4Pl0yj949937GkYFslnV+lncGZU5MRzwv\n7y2QHa5+1eSFl58sl8uygdOvY9mk72oAswY2LQMsFx4CmK570xIrR0S9NhwnAzrJemuxWMRkMom9\nvb24vr6ON2/eFDoy4Izo2j6mDeODLJe4pjY+mc+h+VrmjR237+os5V+18kGAzIj1olrvUmZPHaUm\n9LwDXC26lg2Ptm3j+vo6+v1+7O3tFaXNOU7UBTCYzWZlLae9bdRl8JsJ1CAztwkwAohzhIDoIkQ8\nGAzuHV1iYnd00P3N1yMYnEJp5UI6Xb/fLynFtMFjbWbKhoDnwGOCwHkojz0bYNn4z4xvpeoxoWRD\nxAZANuz47OdkJY8QQqCiTH0PY/zy5cty6DTAvmmaci4i806dbOeeo0IGuwa6ecwsKHGqzGazssbl\newWZ3zpamb93U/X4nAHhpt9qIDODxcy/m4xo5jwr3ZoBZqMa/nOKF/IvIsrmPcwbZ4dxrhzfHe12\ndMDtzmNUM96ZC0csbWTD1/yfxyzXaaWcPz/2qgGyroJf00MGMG5Dh//rwe9qae+2q4yf//zn8ff/\n/t+vAprKTY/WuwlI3/VpLXtqR4w8lAqb21SjOztes0FYqzPTc6b52nhk/Ue/cpsMdDLNZHBop55l\nbI0HLY/dniwj/ZlrqBOHZdOsl5q4XbQ9O/twENFm7gW44uzNOsXtq9F/h8TatmQ38EwDVtpnXZ+L\nacZjVKPNrMtzHZmu6BdAH0CQNzbzMw0wsDVyGu1jYNM08utWHtJtmX83gUtsu7wO005SnpXnPEJ6\nK+lQyxnTvIMW6Jb5fF74jzoIuDTNegd17xxrm8j6r+assNzgmk36Hx7yO3oP4JodJbTdcuFDLMhJ\nlmm97/JBgMza5AJ0IMxM7BFrgOk1aOfn5x1DPBtZWYBiiOP1sCJna/Pj4+NOzr9THb1+zkTMs7KR\n6j76mRbkZhLu8XcKhmoG4n42TIDAN1C08OE74KTX6230zDgloTBVem40TcRX42yPLQrXu7tmQ8BG\nqdOFM81ErHP4Nxm5WfnZwHjI0ObdY2RgORqNyvb1jJvTX29ubuLs7Kw4MkjXQIgjLBkfew+9OVU2\n0G3k57Hyb17b973ugPaIrM1G9sMVPB45eQhYZoWSjfSI+8eZWEFlo9m/1wBmr9db5++qvfBklhUR\n0Vmz5GJDbTgcdpSZnRK+FiPUcor7bZxl2eTUQI9B5gdHnjLv2piv8XU2Dh3ReBuwabqoA8wH0mgh\nq7ewR2sArm276axfp2wClxHRkS/IFtZa1lLSsiODYuMt07uNR+jP8qdmkGYwlt/pg3/nHhtc6IE8\npqaXXFe+nuus8/iNSG9tLGhDpiu3Kz8vz5ENWfM+dI6TO48D8hrec2qoSx5zR0my4drr9TobArnN\n7ldNRtbGlfea08Myht/tJNgkJ/r9fgEMjpbbxrIt5mUC+VXTd/kefsvlQzb4v015TNc9xMcGivBT\nbU13Tb9uckjV7OyItbPU/I7taxCHcxU9dXZ2FldXV3F8fFxkScR9evU6XfRc3tciywFKlm3+zXYl\nn3MwZ7lcxu7ubidDAZrcZF9+32UymcTHH38cp6en8fLly/f+vA8CZLpAFHgvKFlwmgHY1vjw8LBE\n/ACP2YjKCoDPPBvDH0Jy/jhKbDKZFEIm+jSbzcq9NSPUbeA/C2+e0bZtOdzYGwcgaFGINgIBFJuA\nmBUUYwszwNyMq9cV8p43qyHdwef1hfrctu3dXhUVBjPArkWAPX5ZKea+OaJq5dbmtqR5tqLid4Sm\nd2UkAuit/7NnzvNP5JexjYg4PDyMtm0L0CO91x5ozyPpcNPptCxyz22uRYXscec/6o9Yb7r0nZSv\nFbXc/H099feBGp8fA5NZ0UbEvf8pNWWcjadu++4DzHuGXLOOklC/lZ6N+4j7URvqBSw2zXq9io12\nRxmpL2dduJ3QJHTC2NsQzJEaG481vuU9g17/vukdYM37Y5HN3LYa7dx9fQBsvkVks437dX/Tsqnd\nyOC9vb3iMN20iY8BovVZNv6onzmrGYUPRS3y3NbALNdtMtwi1hvlYMi63+6/6aBmnLmf7iPPMMB0\nHx3h8/gbACNneX5tLDPoMa94yQbGaJYj2S6o9c/ADD1TOyfS904mk2LER8Q9Xs9zmefQMstyKc+l\n9YxpirJarcpZh23bxmQyKcevNE1TnCeWwaZTA0evt/Q+GZn/8yZAH6pR/65L1ml5TB8CmdAY79y3\nKUsi85NtQdtEm+hrtVpvVmVgxvOyDeZ7sKvgievr68IPBJWgfR9RWOMt61TTsPtm+jevmYcZM9rO\nxlTZnqytMf6+irOchsNhPHnyJCaTyXcCMCM+IJCZhU4GmAZYVk5MPgYYQAHC4fwqlFAGp04Hgdm8\nLsPexGxUkpozmUzi5uYmLi8vC9h0nyyMKTb0LMipP28iYEBRA8g1I9X3obgymMILauZhnOiDF2kj\nHHweWC1ijOFA37KS5d3ponlu/b0WUbVyrBnD2TClbwaL9I3P3lDDApW68VJdXV2Vlzd2QKEy3mzk\nYvBO+rPnLRtybduWjawoNaOb7zYEzDfue628c+FXqe4+kOxeuLkNdYfQJiBZA5o10JgBJmO/qc6I\n+4eUWw7keyk5TYx6M+15M5e9vb3iFYWmZrNZNE1TNkN79epVAYjecMSGQESUTYAwgperVTSSDaYJ\nyxkbENCWFXNEfQdn319T9hks5qimHU8PRTaRN9nBVDcw3gJsQo7fQaadjRtoiD0C9vb2OhukWF5n\nw84g03It02IGPdaR0I2f0x279ZnL2WFxb/zUPz/Pv0Fv2QmR5y0DqgwwM+/BN95cxGPmscjLadzu\niPUxJ+g72uLICDToZ6DLsmOFtmKj5HQ66om470Anmu0UUvMFv6OXAHQ1uyPLvdo1NUCMMU9bAXVu\nL33/q3/1r8ZPf/rTou/I8uGUAO++m+UGn903R6bop20yZOnu7m7nHM1f1VIDl5t0X+2/rAup046m\nGt/we+06H8+GneH64XXmFf7yRjz8j5xYLBadMzJZwuXof95oJ+te/57lGs7MzIPuq+vMNimlbdui\nt7Ns9HK07xNs7uzsxMcffxy9Xi9OT09jZ2cnjo+P4/z8vBwr9L7LBwMyI+6fP2gAQ8neR9+bQSpn\nTE2n03Kgbw7rWwngEUHZw1RuU07DtUIYDoflYGanPFJMYBboEesNPyKiA3R57/V6HcZqmqaAwQzA\nakCLa3weKcqZ9uR+8RwLjYj1jnmbmNLHtdAep7xY4Vm4ZOcB9/IM2uf+1Ppthc8YkoJGpID5tWGS\nBRX1oLzYydXbpmcnhenUgt2L3QH3WcHyHaPRBjV11iI8NtCtZG3E1cbrnZV3CizroHIToMzg8SFw\nWQOCVrY1MErJzpia8V9rq9dvZAOf382PGGIYTWQbHB0dlWccHx/H6elprFarYrhiBFCno5Gd9Ssa\nZ9Or2w/dZ5qyfM3AoPbdBoZlin+vRTX9yh5hf7Z8y+DzPn2tUaTlbscwWWfavrfCGGOcjMfjcrZz\nbTOebOBlp8VwOIzRaFTSo30Pc+rjTfjdm5VlfvG4MK7eGM+6Ko9l27bleQZ2XG+ayyDTeivLAN7N\nbyyV2d/f72Sb1HjYuiA/E3rJTpea3VGTv8vlsrOhievnXsYwg3Sup61ew5jliQEafYGO+v1+vH79\nOmazWSfLaZP8rIEQ2hqxzpTiPGbaZUeBxxIdy6aNvV6vLCviqBee4ZRiz0F2ojPfBtMRUew0jxfR\nru80Y+c7KDVdVwOCtWsemm9KdspkJ5ZlSea92lpL6zlKnmMD04iu7Wl7GGd8trmzvLejx8/PbfMa\nxOx053/3uea0zICadmCv5Tmz7nW6/Xu1x1K/er1ePH36NE5OTkqbXr9+/Z05ZL4xyGya5icR8T9G\nxMdxp55/r23b/6Zpmv88Iv6jiHjx1aV/p23bf/gW9ZV3g0hPZC4dY6Jpoteso2YR65SRyWQSTdOU\n8HtmTkcVIEQLL6cx2tueDbB+v18AKmmUeGQoVnYQrBkpe1dsPDqVyCkKBpSbxtWAyfewW1dOSYlY\np1h6PrJw4H1TlML9pj1WIGZmGEKN7yhW6jFY9fMZN8YOpcf6yZq3inEHNDtySeHg55ubm45H2cLI\nBgRjh+HHmsymae5FgDNd0H5v+lQzBLLhTTssBFHm7q/Lt/Kqbbi1O+fdC21UrstdhMn8XzOMNinL\nbDzVgGIePyvZTdd+dWGn7TYs3RbqyM6KmnEY0fXawt/QxvX1dakPIxoDdjqdluvH43GRbT7bdhPP\neWxpQ21M8ni17Z3zzZ7fh94ti0yTNq5Nx1le1Az4GujMa7WykeL6eW6Wk/nzeuAKaX798hYRUbJq\ncHxhdGXHhSMH/swYDofDzrpw+pEjeegvR0ktD2q8ZtnGODmF0ZuZueAscTZQRJSlK9nwi6inzGew\naZ7r9+82rjg4OIiTk5N7DptM59moc/+tB3DW8B1etkMQfvBGbuj9tm1L5I/n27DMRnK2a3zObcT6\nPNoso2r92d/fj16vdy+byk71mmzMwMOgF2enwR/2j7NxVqu79MU/+7M/i1evXsV4PI6muXOsXl9f\nd9JkNzmCau0CYOJE5Xp4IafS8rxf9mLazHov65w8j4/9Z/qrRSZrADPLHzuRNvFabo8dW7xwfNmh\nY4c+92OP214n0OGjv/Lyooh1mrv1LYX6c+Yi7bWdZn6DD3gOfcn6Br1pGs62Ww3IvsuyWCziiy++\niOl0Gk+ePIn9/f148+ZNXFxcvLdn5vJtIpmLiPhP27b9P5umOYiIP2ya5n//6r//um3b/+JtK+r1\nenF4eFhC6dnIsMJhUjvGYNSNU+5HCO/s7HSOkIi4IxKIGIBIQZnCWChr3vPCcwip1+sV42F3d7fs\nTgvYtFcaYs3ePXtvuA9CrwHv7GX0mLhO8uFdbw3kZmPVTEs/B4NB54gXDOXCPHc3V+fIhrSZuPP8\n1IaaEZnpAI8qxrcVvg3RLAgtXJfLZSeS5OMguNb15LazdgCQS7qQIwHMIXPifthIrxmDFoJ5Ywgr\nEbcre+6o72uXtwKXj392f/LLSjYb1jVDaVMdeUwNwKk/X5c61bnfxTRjo99p0AZZvt9OilpbItbR\nbPjNmRg2wp0qa7CW1zDZyLMcqc0LdXtTMry81HHPIRR1UJfp0oW6s9Ht9qOkzWtOFbTSNl+b9h9T\n5G5bZ57fcWSzae5SnmsGVwaGpq+c0orcBGA66pWNxYh12nY2BmlTplUb9DY0867HPpKJ4iwcaAhA\nYB1HO0wbWZ/7N8vf8XgcJycncXh42Dkg3fxtWrE9gQ6Atr0uFV2GsYoByzjQJqIszMPBwUGh05yK\n5zHm3uxcYY6sf4hImi6yDMpAjcg4RjhO0ZphnWWtbSyuI1Mq21yc1ctvAM4f//jH8Ytf/CKePXsW\n8/k8Pvroo0Inl5eXpc0G7dlJUZPvdljRFuStd9Lu9++WR/2yFffV+sQgLQNOz1VNP1JvbSx5Dnya\n5VF2eHnZkAMy+dqsezNIxRnK/RFdx1Dbth15wmfrRJ8hzzI42lWTH5YB/s90DX2R5cdRh/znsbPN\nyf/OVsrzip63HDJwzcsQ3nVp27Y4BC8uLkpq+XeZVv6NQWbbtp9HxOdffb5omuafRcSPvkldvd6d\nR54JiFgLUHZzZTJqRtwqCXTX60jPYDCI/f39ku7o8xpXq1XxuhF5Wq1WxaNiJsCTSnQTMGqigQjH\n43HxIl9dXXXqs6DIkYgM+PCgwvg5JdUlR0pstFMf74AUziL1LmB5TFEoKBpHfW30WZjZgMtMyJxk\nQ9HM7LnOnh8bo6T+cjwIBqy9sBa+Ni6oi4glaT72gJEewv2MBf3HCx2xXi/AzsQR3fPRaikgNrh4\nRi065ef7fkdSs2H9kIH9aHng1ho4qb3nUgOC/i0b21lx1UCl6629SluadcZDRErN7nbmXlvdHrfP\nZxe6PssWZy4wd979zhsa0F7Tr9d4wKPehRpaRRZZZtL+THPMEc9z5At6ZOOrnZ2dOD8/LwaA66kZ\ngpYBDzk/GA/LJtpiwGmQgLyAjyx7zO82DDaBzTVt3EXU3yfYHAwG8dFHH3Xaynjn9FiiY2RieIy5\nz2s4abcBZqbbNWmvr4VOMw9ZjjOGNjSRh7u7u+XMRgwup+7u7e2Ve/O40hc/Oxc743AW7+/vx3g8\nLjTifnEtvGEaIgq7Wq1KmjFjSGQ5O40BW14DuLOzU/iAiCprJ2tyZ7VaFX1SnD9fkRPzxDsZM47e\nwD9eH2n9yhgwJ227Tl9Fp5mus7ytAUw7HHq9XmdfisznjN1HH30Uy+Uy9vf34+LiIl69elXG+PT0\ntGO7+fk5g8olj6Xlh2UV38fjcef+b6X73mPxnGdHagZtvrbGq3k8rX/MU3YcbXJm5d8tm1jmkXX0\nY47WplnvlFzTbbSRTB7018XFRQnyeP14xNrR5Swb64JMT5YNdlgZbAE0rXPon4sddAbRWcfAN9SR\ns3I2Ac73UQCb38dJA+9kTWbTNL8ZEf9aRPwfEfE3IuJvN03zH0TEP427aOfpY3XYU2ehh2C14W/G\nW61WERVvmAnJnkyU93A4LFElh9kRyD4/E4FrMAlDcWDs3t7evWMFuA7hNxwOy2Yc9DP31/dYkCPk\nAToUC90cSbDwtTLxwn2nJTPWhP5tnDOOPBMGN4MYaGVmy/PLmHrOEBDulw1zj5OFwN7eXpkHRxn9\nfEePLaiIWOZ0WO6zAIT+LByzkYOgOzg4iKdPn0bTNPcMDF9P3/K42DOXIwCZZ5hfjCoLPADKu1S2\ntT7Ufsslg8SsUK28NilcX0udHptsLNGerIzdHq7pNfeNXT/H9EPKqtfT+VrqRObY22lesMJinZHX\n45onvFU6jrMczciRgqzg9/b2ijyx88195nrojqjqZDKJi4uLEqn3MUQ1EJOBZzYusqxwPc7WqAFO\nGxZeu5M3qbEcyx5jj1tTNgh6y8jmN2AnnJxsRkc/MNKJEgBcDg4Oikxr2/VW/Rhe/Me8M352dmRD\n0/NkvsLQBKDk+fDc+XraPJ1OYz6fd5aU4Jy4urq6xxsUz0emIQMaslO8vjT3m2uR1c6yyXrZ9MR1\no9Gokx5qGkUe2xg3eCbK6/5YL6IDPB/m79VqVXRQRDfLwLLAu7Ta6W4eiojikJ5MJmUOanJwk+zM\ndJvBCoAcR3qv14ubm5t4/vx53N7e3ttUBHCeed1yoQPC01wxdnkZlemX+fs+i2nETvdNoIx7/HvW\nk/m7n1UDnP5e41sDSDsWLTcy8CRzIttR2YarAeT8XL5HdG2gtm3LufROr4ensTuzAwkbB8eM7Z3M\nPxHrHf4j1sepUCeANgPGTaDVtFhzAEGz1teZpjPgfJ9g8/so3xpkNk2zHxH/S0T8J23bnjdN899G\nxN+NOzX8dyPiv4yI/7By3+9GxO9GRPE+Za8mA21BxoY6GGsoBW/hDQMwcdQFsRrAsvsndTLJV1dX\n91KM8hoU1l3a2ET5GPhR+v1+TCaT0g4zyVed7jCo25+NQYjb45QVDc/PgC0iOgIQo7Nt27IxzvX1\ndQyHww6otsDMkYNswJlhYHx72OlDFlL2NtEn/+82AC69+63HjDGyBy4iCqicz+cdg88FIWgjoDNX\nahOF/w8PD+P58+dFeOX/+Wzgj6FpI8Rrn/JOyBZ6VlLeLCYbRBHxrGmafxqhw5MfKJmWHgKUD4FY\nK8kagMxpNzXwmevZZCTlseF3C/38O3NRU+BEHABZpKSxDXhWuHm8sqFhw9VphdApkUNvyU+dgAwr\nMepyKhGFqPxqtSrHO9jxYmOCtjlikY09ZANH9WQnDfW6/5teEVGMQjZmyzRUa4PlK7KC35A1GBtW\n2JZ1GeDyng3sR8Hm25XCc+gIO5BGo1GcnJzEcDiM/f39OD4+Lqn+3jGV+bm4uIiXL1/G7e1tWfvm\nlPucAUDJxh20Z6cJ12fP/Sajx7SDrHMEkLRK+CvifjYNY2ze9fg3TVOcudzrTBU7IxaLu6PLiIDY\n6UB7vTZ1uVx2nL7oFPpj4xY+tt7LQCgiSpYB4+HdWH0u4PX1ddmZPNOtjfHVatXZOXW1WsXBwUGM\nRqPSZu+uaz5nXOnTbDbrjGsee7/7d88dPMdzvYnZ1dVV/NZv/Vb86Z/+aTx58iR6vV4BxsiOmryt\n2RE5S8O6PdM655k/efIEOVR4jmu+q9Lr9Uq0nQh4RHfJSnYAZcegI5u+3vIoIu59d8l2gvmUd9pT\ny3xAn9ih6ogmewXYpjGd1Pjdstz9pGB3Y3OyJwF9zfKNz+ZDOypyhgc8a36mn14CsFqtijy+uLiI\n+XzeAZCWOZ4j/rN8p63OjnD7rJ+yHjMw/mUu3wpkNk0zjDuA+T+1bfu/RkS0bful/v/vIuJ/q93b\ntu3vRcTvRUScnJxUOcVKstfrxWg0KsISIvH27841ZhKzIWbhayPLwtyEmI2RrIAjojAFQncTY5kI\nDY7LmhYLDClYGwdeC4Lh4f/ov5U2fXV6KAxC2lm/3y9ghhSi/f390hwD+9wnf87GtgWAhSVtp60o\nHjOwhZeNHJQK4+1CO6wEAcUXFxcxm806Kcv5ORTm3+OZAY9pgzoODg7iBz/4QQF8NQMqg0NS4qBD\nnB4YRQb1KN/cX7eR9mCIaeezl23b/rWIiNFotGl1ZSHFrMQeApm1YmVae3kdyCYgWhv3bPxkAJMN\nKN+TZUrNwKLOpmli9ytwSQTQjg3X62e7XVkRew0ykQDoFJrOY4pRyr1XV1f3NuQwQASkQgPwiCPi\nlh9WzLzbOWEHBzyKgUw7ALReA5YNHcuJpmkK3dOWfJ4a9yLjXIcjmjlyORgMOrvu5v/9PY9zrWwE\nm29XCs89e/asxanJGD1//jw++uij2N3djZOTkw5P2MjDAfH8+fN4+vRpfPnll0U22ankcTKPua7s\n4Mmp3nm+GDMDLMts7m+aO2cly0KYGxvNm8Yvy1Pe9/b2ytmLEVFAusEaMv3m5qbI+Oxk4BnI26Oj\no7JZDrrf681oJ7oCoIgszrJmuVx2QIXT8iK6kUl2KSc7KzuhSBeEH8yD6LGTk5OS2hcRnei37STa\niX7hOCTTcg1oGgha9jI2AA8Dv7OzszJmx8fHRUa+ePEiJpNJJ4MIWZMdExH30xEpHnf65B3jkdHm\nuZ2dnfYhHfWuinnp4OCgbLJCuy1vrJMMtHA05NTkWjCB92yDRXQDCxFdEGnH0CbdbJlAhJjP7DNh\np33WeZarfgb/E3WEj6ELZBx8OxgM4unTp2UDTejOOszP4d3jxXfGDL2QdQy2LWn4l5eX94JC0HPN\n2W9cgF7i2pwyazAJz/vFePHZTq1fxvJtdpdtIuK/j4h/1rbtf6XfP2nv1mtGRPw7EfH/vE19FjIR\n94FFxHpTgYj1bnXX19fRtneH/47H484GAxCAFbUNLiaO9SSkH3nnOBNjNlbdXisXGDIrbIPXvGU9\nxE/fo+16ErMBkBktp3haSdjrbwFn49R9weg0wzv1CAXvsbGHjPGBuQ2assIykLM3hz7SbrzMeKKd\n8uCSjyTBs41R7F33iB5a6FoIegwtPG04mEZHo1H84Ac/KBsSWLEYgCyXyxK1Pjg46ACGnNpFGxg3\n0rLpR16TAh32eusjb2oG9f3Sps91gJk/10pNgZk+8iYDGVS6vxls8httsJJl/vGsO52z5nTJDg8b\nNDhZnJo2Ho/L+itHRMyf1GGlgGLMc+rnQxde84hsmk6n0bZtHB0dxc7OTvzlX/5l3NzcxGAwiNls\nVmg4R9zx0BogegzM14yPN9PJ801bcepxTix9HI1GxRvsjJEaaEEuACKapomLi4uO55/3/DnLiRrI\nhM7YoTNHNB0dqYH6WnlrsEn4u3I/m881TRNHR0fxox/9KA4ODspOsdnIyOmD+/v78fz587JGFsCC\nTvE8ka3A5+x4gR7Qyb2GBgAAIABJREFUPchkjx9t4LsdDgaz8JxTbZH/OTKTacr6DN2C/sHI4z/A\nxPX1dZydnZWo+mq1isvLy7i4uLhHD7SRPsBX6ACiJfAKwBldYLo1sIvo7pZKG7Ox7755wyRHWAGy\nFxcXcX5+3qFPxok2zOfzmE6nJTLoFMLb29vY398vtOAIKCCMtHzPRQaZplnmhHeABun76Jn5fB5/\n/Md/XGTWyclJkQWks9N3g0vP7yY5nR0orDlFBnNfdjq/z2JABkBCbxwfH5e20Uf6bAclNEld8JuX\n79RsNsYBEOOlS7bJDGSznuLdR5HwnTbSJ2yvrhPmbh277U0i6dl24WVao69+3v7+fvT7/bIkYDwe\nl7HzGsyI7u7LBnpNs87Mg5ay/cO4RKwDN7RtZ2cnDg4OOunryBmn6VN3LhkvWP/4HdCZdZgBqUFm\n5pdflvJtIpl/IyL+/Yj4v5um+b+++u3vRMTvNE3zr8adqv3ziPiP37bCbOjVAF2vd7fOIGK9zXjE\nXaqGzwuz4WQihpB8He+LxaIIBns8mmadNpfBZcSaqLKnzd7LDBbsrer3+wXgIizoq4W8DWYMBwjU\nhqsFG/XYKLfAMpGj9FC6V1dXRQj0er3idbWxFrEG/yg0A4vspbFxA/BHeHicrICHw2HZ0MdeP9eX\nx5eF49PptLPOxYLa0R6MMI9DjoR73m10zefzGI/H8ezZs844OOLI/YU2vgKYKN9sFNnQN82xNuLm\n5qY4WkzLm9bs1kDmWljVUl7fzvB2Mf1lcAm9OppSA2gGXTVe8xjynWtRhgCY7FHN8iQDPYyX/f39\nYpAhKyaTSTF4UT7ZSKAefqOtt7e3nZTtDDTdHzuaTIcY10QJptNpR/Fkx49lAM8sBkuv13HuRESJ\nCOGMYfzcF/rGOLO7KZEjgM5oNCqRmjxnpsfFYhHT6TSePXsWz549i4iI8/PzewBkE9DMfbXy9svp\n5jUQt8lT/K3AZuVWaHQymcTR0VF8/PHHBWAzxjWeMPCDNkajUWn3/v5+Zz0SdaxWqyL7svHusfSL\nZwK+WIeFXLU33wCIZwIOMUYtlzeNVQZiAKL9/f0OD7BT9/n5ebx8+bITsby9vY3Ly8vOERa0i3cM\n5qZpynKYiDtdsr+/X2jS6aB5PrweDJkWEYXHa45BPweQxdhhTNIX1qN510zAPvpqtVrFdDqN0WgU\nBwcHpW52kFwul531q3Y+sjSJ6HdNljIn/GZD2bu20zac9IPBIM7Pz2M4HMbl5WVxQAGs854HzDfP\nMRjIdEnJkTTLbhw477PAqw5aZBBGWjz63bocPjWwo45aJtzt7W1cXV2VNc/WaTgJDdo8b7Z37Oix\ns5++IEe98+ve3l5xbNbWb2Z7iTZbXph+3C74nDqcWeN+ACpxolr+39zcdJw+dlDBy3ayZB1kYGzA\nTl20CXo/OzsrGMEOWmMW0wk2rfvraKZBZC26mR0P/uxo6odevs3usn8QVX9tPHomZi4WYqq//Jee\nG01zl7bG9sZMKF59GNueGVJZcmoGQmM8Hpf1edRvYBXRtRuaXq+b3hr3D2w2M9YAc05tRWhBwBHd\nKIjBpTcusoLOHmieB0NFROcee2LxSLKWhggK/beRbMaFORECKFgUH+00wKdfTlFyvRgDeVOVLEQN\nTCOigC/ApT1W9nrZsMk0wbVZKJlW8dz3vgKLh4eHRZkTjbJHz/SLh87pHjlqwXOsVFAaGFukhxlE\nI7CoY5MB7bHM7fs6xUokg8sMMB29f8izaicIz3is7YCwiCjK2I6emrIzyGuapkToGGeMTGj19PS0\nrGvid3jNCocIEqlcGJ94QjeBcNMVcwe/LJfLmE6nnbQhADFpWdnZdcfPEW27Vl45+j6fz+Ps7CzO\nz89Lip6dVvQFRey6nUrFdcgCNq0hlZFxZ4wclTk9PY2f/OQn8eMf/zg+++yzKtD0vBfHX9tda077\nMth0+qwVOfLR7w/xSaL8aJrNeqp2X9M0MZlM4smTJ3F0dFQMdHSWI4huu6Mllps4tFjH6zTL6XTa\nMZgygKV9GVgwpoAhItTskg7tA7j8PfMuYzybzTrRpgx0KchcDHTmBSfs7u5unJ6exosXLzrppKZp\njHSPmfkF/iBqSNTv8vKyRMciokQaWeuIUetN3GqGocfR475YrHfOhK+YL+9kjt3Q6/U6zko7HtGr\np6enHVBg/Y5O4Fm0ARrEgVxz4mUnCu25vr4u/O77iGhiazEHq9Wq6GKeZYM8y/nsPPLLdhygCEBD\nHcPh8L2BTNsqXovqqC4OJGQ+a/YtW0gnhwec+m2dRWYD0er5fB6Xl5fx5s2bOD8/L6nQyAHecVZk\nB2rW0RFrB77phGsODg4KwLSONBC1w4V623adGZgdsBS3wbannVc4zLFJ+WxasdyJWNtx8Lfb58+M\nNXjAy6+sU6gXG284HMabN286xxra4eQ+MvZe3mSdb6DpAIP/t57Mn7nmlwFsvpPdZd9V8SS1EREJ\nDPAOU6HE7PXY3d0tniSDPYemI9a7M0JUGO94AvF8DgaDotA6bb1r0F1bNckWkmYWe5ZMkPZyWSnB\niLTbjE4ELQMKxggDGQBIG3lZIPg/nnN5edk5UsHeKoxJ2u7NSNwfGMsL+GmbwSmC1v2g/znFoNBA\nAurU5XPbEEy1vttAtZLLNIjQ6/fvts531JH2ssCfthhMe0wovd467ZdraoKCMcoKHzrH8H/27Flc\nXFzExcVFB1RRaLMdOBSPy9ctWWllUOnPNpCzkqsBTMbJ77ndjCkOCc6qw6HBHNHW/ByX3d3dsj4L\nJwtecXjYEQgUshVZdqDMZrM4Ozsrnm3S+WmXFTAglf544wDvWG3wbJ51Gg9r09bpU+tNBjCU2SDl\n6uqqRPsd7XO6P21DpuS5Qo7SfmgbYMkRFxh/9tLSrvl8Hq9evYof//jH8Zu/+Zvx2WefFa+xZVuN\nBiw/I9Y7BxLRQ1YyX065gi94oU8ec8rc0RQ+xjXYrLXTpWnuUmQxHGezWYn8AuKcSpfBYcQ6Awbg\nBf1ZXyG/GW8/n3ot/zt6V/KAz5eXl3Fzc1McB5n3aTPy1OCYaHUGNJZrvNPWg4ODAiyInDdNE+fn\n58XIY50TgIMlBHY64ABytGaxWJRI63Q6jcvLy7KG8fLysvznqBGy3hsE8h39NRqNihfaMp2+kf3A\nb2waCPDr9/uFHuAZG8+8vEst7ce55rm0fMGQpm3IJMBOpgHmwvQOYAJkWA5hI1A3xjUbetGemtPQ\ndG6Ht9tl/eH7sp0Ucf/czXdRnJrLiyjy/v5+ASiATsYDBw10Z3sC+ZidPwQYer1ecdA3zV3a6pMn\nT+Lp06fFuXx6ehpnZ2eFVnFsTqfTTv0GnB4/6MOAcH9/Pw4ODspGm3Z6MM5t28ZsNovLy8viUM+p\nwRHrNZZ2tsL3PNd2mbMwaB/y3WDczkTu9bNNOwB07EI7USk4eog824Hm7Je9vb149uxZXF5exmw2\n62zOF7EG6n62nW3L5TJWbRtNrGUTY4zMoB+MhXXcpoim7e0PsXxQIJPSfjUR/o5xxsShZL2Ri1Mq\nTZB5xzcMAnv3DfQyOKBObxhDuyg14WaQ50XOZkQDCYMeDDQToQ3RGni00vOaRYNpP8PADa+MjUc2\nQ4iIDpAymOJ+CyCfzWXA7f7aC2OngT1c3Ev7bai4Xp5HnTUg4We7vaWudA3v+/v7cXh4WJSF547x\npm0dICz6QIDz33g87oBjz9Em0Of+oLAYr6dPn5ZIF8IZOrFizmPh97ctHrvaKxufm6KXdnZkB0nN\nK5jbDF/gdT08PIz5fB5v3rzpKNjai7HESTCZTIrwHo1GsVwuyxb8RDm8SytyyGAu04T7c35+Hjs7\nO3F8fBzHx8fFKHGbPFfwqPnMG0KYRlGgrJ/mlY8XAVxdX1/H+fl52TXPmQaeGwx5G/tOU6P9rKvk\n3FzSBvm+XC7LOlbAhiMRKNrpdFoOcv/JT34Su7u78fr1684awMwnpsXs1XUKOfyHjAOQUDfjjPFn\n7/JDvHM3x91Nsh4Cmk3TFECD4c86TNapkUFjOqXtdshlOe2on9c1Wb5QasZnBpbQDu2GJ9jV1tET\nRwcYb4A0AMw6gDqzMbharYozDzpiHtl1EtnnqANrMTHIoVk7ki1vZ7NZAQDWc9AhDic2tsqRQd6d\ncrpcLmOx7EbRaR/AgfElNZ2xwvmDIetNUTA4s/zDyL64uChyo0uXTZFvjOFoNCr3EbFlI6DH6Ja0\naa8RZEz7/bsNUtD5y+WyOCZqAGeTfLfuh+6JXPpa6Nz0yXhvcqZ+kwKdEa3c39+Pk5OTePr0aRwd\nHRWHC2MAvUMzAPCI9dIur2W3DZed/rZNkVM3NzdFZuzv78fHH38cFxcX8eLFi+J84b/pdFqcCHkO\nrKNpMxkxtjkjorPGEj5gV/7absieH8aBLEN2Zrej1bqEtrBe2oEGO9lMO3ag2z6DP6fTaZydnZUs\nIGQI/UPn7e7uxsXFRdnYz+3zUq7BYFCimk5ltrOIdtneyHNpvWo9hbzIDs8ccMkg04Gn9+Fo+Tbl\ngwGZWRBkI9iK0WAkIoqnA6J2ygDF0ScmGcbB03l7e1uUJvUiROyh21RolxnaANJKlbr9O4Rt4vQ9\nNpRIQ3F0AybNKSQYxgYwBni0B2PM60EAmRgdNhDNfAaRTksuHpxVN7WWvqCAHXXieQZuMCBGPGPj\nCJOFHG3JCq4Ygk3TcWQ0+p927O/vx9HRUQEUGOIcBn51dRVv3rwpY+cooiNerBNkHVAGpKYP2udI\nkkGF6Zb7+/1+nJycdI6b8Ty1bVs9rmRTxKZWauDSKX7+Dxqy48C8kXkk9znPmWnC49br3aUdf/TR\nR9Hr9eLly5dFQT0ELrmfqBIGGOlWbKpDSpuBCnyEYrE32srYSgae6PXu0qkBtqxrRMnacBqPxyWy\nUXafjm6kyTQzGo1KtMTKKyIKkLy5uSnpVla2yAl4GtmAoYSRR4qssw7IIEB2AjBsJCyXy5JmR/SG\n9luxTqfT6Pf7cXh4GD/60Y9ib28vXrx40WmTeYE6IqLIK2+AEdHdkMypzTs7O8WzDcBEZxh81rIM\nmAfTUnZO1YoBsT3VzvpgjDFCchoyAAQwihGb08FYT+kIn9ubo/z+3xEl8x8ybLlcFscbhpOdLU7h\nbJqmA2hsMFpHrlar4qxYLped4y5ms9k9fe/1iUQV7Ii2oZ5tB8tb0uOvr69LVJ0xNmjxPPFMHAO3\nt7dxdnZWlkx4XlyH9RW6lHrRuY6qW6eZfuAX6kN/2riG/66vr4sTjTFGbwP+rVNNA9QHPw+Hw2qa\nLWCq1+vF+fl5yVxwH7LdVgO2Bh+OXFo/5Iil5SZzkcs3AZn0++joKJ4/fx7Pnj2LJ0+exPHxcRwd\nHRUHBQ5I+mjnC/TV6/VK6vnR0VEcHh7G69evYzqdlvbRRoN45DIORNMegJP2XF5exunpaQH34/E4\nrq+vS7TR82uQTx+ZP9MiNjEReJxhzLFtJc8bbeZZOC2JJrIu+PDwMJqmudc+eMMAzzKfOplvBwsY\nJ9Zos2EP6cboEngS/XBzcxNnZ2fRtm2Mx+OyLwP6mWtsJ0dEAZwsW3If7PTlu3k8O0/pl0GpHXc5\nqmn7GjmDnuB/09b3VT4YkMngm3Czp9Mek4iu58JKjUmwwsEIAqR54PFYkn5kDyjFgMypSDVhSbES\nNZiAiSLW6zJz7jpKzkYU3ulsJDJufkZWtjnlxP2zgWxCN+PboLbicPuIKuajYHjRBy9ux0i1QjXY\nttcJAxbDCQHIGFtJ2tjLgKVt22hXq7vNnCrXRKzPRYyIToqS0zb29vbi8PCwGK2r1XpHT9KddnZ2\n4ubmJl6/fh1te7dDaI3xPdcZgMEbFMaKPk6n02L0XV5elvkw0KyVtxU+FnqAr9oaMq+ZcYqNjYza\ns2lrds5YBlj40n928x2NRvHZZ5/dO9vW/On6UPYATJQ4xoKjcuzcae8i0UPanUE2StagEz6cz+dl\n90j3k5Q3G5vQWaaHTAeeH8swO3dQuvAmIAeQYMdQxHqXZniati8Wi3JmoQ0rt9trMuEHDPfDw8MC\nCqjf84p8Wy6X8cknn8TBwUF8/vnn5dkGUtn5Qrtpgz3GEdF5ntc9EpFypopl8UPe4ayvHgKayLac\niQBYMN+Q+sp93oiJ9pjv0Cmec4xUomPZ+WGj005I+mD57v7aeKIOjwNRRuqwTPB5qFnHc1SJnUTs\ns4Csv7y8LMY5gD3raepj7hylz5kuyHE7nu1Yur6+7qzLpE7kPfpnMpmU+bUssz2SMwYi7vQfRjd8\nRrvcL6+XQ15Zx9IHO9eRObe3twUUGWhGrJ1ZGM/cR9ud5ojDlDW20C50yhjhHGMesiMu62OKQYrH\nDX51vzKPZufStylEq54/fx4/+MEPCqj0+vrVar3T89XVVZyennbkMfYkY+RoHf97HO28tQxBrmb5\nTJ+x7cjmAQSen5+XlF42YcpOC5ydyBQcRE4f9+7NgGrvAm67nL6QXkzUkgzDnNqO08nHocDn9Au+\nJHLKNdjCln/wDvYXeoZoI1mPtnORR8YPAGI2ZcOJT7SetjFmgH3s8uxIyTaNHS4GvFxjZ7YBJLye\ngza1KKdtgIjuWbTfNej8YECmjcBsUGEMesKytzqDKoAjxMfveGx8htRqdXf2HAbm5eXlRmFlQOY2\nUkxAbqvD6AZcMF4WvDaQUfaz2SxOT087Gx4BnGsAtgbM/bvXJ1kZYwTZq0bbKfTbxoa3wgeMMVY2\nDHmW187xbCsQQLGNMdrvqI3bZIBRK+4D7Tf9ZaPfUXGvYaDtFpyM2WAwiJOTk1gs7s40e/XqVSwW\ni3jy5EknUp7HkrotCBw1QpBYATNWNzc3sb+/X4AS9ebIae25DxX6vQlcYhQYYJoHauDSwNFGWQZV\nNYDF9/F4HM+fP4+9vb04PT2N8/PzDsCsCfeItefWXmfPpTeqQcFmJWTDKnsQkUWOHPsz9OfNUHq9\n9SHwPMPKIh+/YwOEKBep0sgKngOfn52dFcAMb5mfoCWMQ4yAvJEEBrjX4THHe3t7xaBAhuIRRj60\n7V0KH33ys6Fr+GaxWMQnn3wSh4eH8eLFixI9xgFgxUuxsrVx5HFzxgG6AvDNWlU7r5CDmxT0JqBZ\nKza2V6u7VE+cEqxfMkixbuN++umlH17jAyhFVkDn3OP/akZ/7kN2CHHmIWDCESZvzILMAXQ6umb9\nuVwuyy6pNsrR4RF3m9CRAgifRaxTKu3R99jRPtMZ846hOp/PC4Bg7C2H5/N5J2WZKE3EnfMV5wm6\n02nv1n0Yp+g7j7OjD7ZraDPZS3YIcf/FxUUBLnYEMt+siySiiexD5gHms/5kQxvmxPJ6uVx25O3t\n7SJWqzvAjMwyHdsplefGUSXTN791AeZdijp1IO/ctlxqzpJNZTgcxpMnT+KTTz6J4+PjEsWFN/mO\nzo+I+OlPfxrPnz+Pzz//vAAUdv+mf7bXqMsym74gC3x2NuOPrPLacqKD0Bbru09OTuL8/Dym02mM\nx+OyBpgdf4nAeX76/X7ZcIsUXG/atlgs4uzsrEQ2GVvqwA5w4IG5ZL8UZ+7Y5kNmsLkWQRScOZy9\nazCadR1t8hpMb4YG39AX7HCfJAHNYUexSdbl5WUcHx/H8+fPYzAYdJZn0QdkGPxm+ou4nzXCmMPT\nlreeF8YQOvc7rww24W8Dzdo98PX7BJ4fDMiM6Hq1sjf2yZMn5QBqK3wrYYM56sCYWgvD2yJAMdxg\nFNY2ORpg4Wqj2akZKAqEtyczYg2CLawRGtxvZW/hHXHHEAgubx6QPXcmSjxHNtBN3HmsnQZMHSgr\nP88g1p5cvMw8yy8bRE5hoj4bKFYyCBo2Y2AcIqKk3+UdxzYZgRncfDVgnTGgHgwle/4Zs5w+ZePA\n4w89IODwhmfQVGuzx8BC3ODLhjlGJWvf2FWXtgAccnlM8TptyVGT/PJ5ZX5FRMf4czHIM1+4/3y2\nwEWRPn36NPr9fpyensabN2+KF9bKL9c1mUzi4OCgE8lifgE2XoecjUDoFyXieuxYsmHK87OcWi6X\nZQ2NDcKsRFgHQzrfaDQqaxvdZztxvLsxjikMe+bQSgbQvlgsOoa8x9Fj4TXulkFt2xYZSmQHUMJm\nbJxrfHBwUIBmTXnS9k8//TSeP38eH3/8cVxeXpYoZ04ZYozt+ccQA9xm/uR5NlwwKNhkhowEGwIP\nOWweA5oYXNRH9JlnXFxcdPQGkUjvN5BluqNcBpaMgcE1Y+TIiMeAZ1tXWG6Ox+OSZon+JDUe45j7\nrcOzbIAeieRZT7NOFePz7OwsLi4uiuHH9U3TlOggc23D0ZEVIks8J/McwJbxtuENKGdO0APZaLTO\nYLww7qF7aIS5ItLIb4wrNGJjlggP7aaN0AEAiI1pvOkMa1aPj4/vOW5x4ptPWH+NHAc8Y8AyZk6p\njLizO05OTqLX68XV1VV1yQRr8xw9pw82iu2oXuuDiKbpOi4N0mz/MdZvW3Z2dsrmOvAkxw2hE5zR\nYuD27Nmz+PTTT+PLL78sbc+ZRNgD0BH7CFjHrVarQrvQNNczr2SSzOfzIq/tdOv379bI7u/vl2VV\nrDWGfvKafdLrp9NpvHjxIs7Pzzv2B0EOwHPW0bYVsd0shxy1gyetK3FEYS/xghe9ZCTbzYxP27Yl\nasmyF+gSu8Hr8cEH7JHB9Thcbm9vy5nETdPEq1evYjabxQ9/+MOyMVIGcWxASLaF9UF2tjC+yMS8\nj4kdi7bhrPeynPUYea6zE8t2Wf7+rkHnBwEyPagGiwjv3d3dODw8jNPT06L0LbCzJwvBSpQOYoAR\nHaFzCgdefwSqo0gUE7WVFILbz+F6nodBSXEbeR7tsBfe3vsskCl4Zg3avNaC9log88pRMdrt/yPW\nHkEUjQEYwjOD6Aw4HdmqRbsyLbRt2zk7DKG1Wq03YbEhldtAX/K4tW3L9pCdeaVdtJWUDZiVNTwG\nvHjbuAfQB9DASw491UpWhtmLxW+uw3UxR7u7uwWQ8H9WvNwLPfqYHgoGLmO+s7MTJycncXJyUgxe\npwQ5OpKFKmNnoxbvsIGXUyFNhwa77LIXEfHy5cty1EBOL8+OG3Z4ZU5Zv9e2bdkaHmXocx9Nw3hU\nnU4HDTK/jIsdMW6P+c+KgjnB44uC9rlwT548KW3B2UT/qM8KhxRZDBnLRtMGfbaX2rSF3LAzwLzv\ndVDIC3j26dOnRbZBbyhxjKCc6maAtlwu4+XLl+Vw8ydPnpTjVnKqcvac2ymSjVmDKeSzz7kjlewX\nv/hF2QQKWc5c1Xj5IYDJHBCRgIbsVc99apqmc2wG6W33je81fTn6bjBjeejdRgFYq9Wqs5N6lt1s\nimG6RjdNJpM4PDws8tky18av6QAQQionACRivf737OysbABlBzDpb+xy6XRAyzu+ewdf1sgjd/zC\noPNRRvAlfTM9wROOrqKLsQUMAPjfwJj/SKVnnZ/51GDWDkaDTNdDFId1ZTh4Tk9PC+/ZcPUOsY42\nwc95GRHOTOYf2mb8cApdXFwUuQA/snGU9aXpyXUZoNno9n3Q1iZAaUC6qbBD+eHhYRkzdo4HlCDb\nSQVlXHEGPH/+vKy5BeQjHwlmZBDhtHh4DnsKx0CWjSzTOTk5iYODg5hMJgVUeT1ur9cryxYODg4K\nXxPpA5yOx+Nomia++OKLsoEQtiO0tlwui1MQe9cAFeee5RYFmcn+AmdnZx1bFH7ifqfMelmKj/ey\n44q2TKfTohuojznKS7SsI60Lbd9dXV3Fixcv4uTkpPT18vIy/uW//JfxySefxNOnT4v9ELHeyZ/N\ntuwMNu/bxrHdZUexbRnogjaYzzzXXJfr4TfoFHpCBvFuGkJnGGN80/JBgEyECcLZwOPp06cxGo3i\n9PS0pLeY2FA8CHoUAQMKMVoBeeAQaBFr4wTlbyWTkX7E2tMGwRisZcOetuABsRFi72gmrOy1zwTo\neiBEKzQzYcR6MyN7TTK4pP32dnm8DCZyX7jG736GlQbvVgL0DWbC8Lm+vo7hcBhHR0cxmUzK/CJI\n8PjaO2rBHE0TTRorOwIyKOUdZYZ3EPqBWS2I3V8M7X6/X9YmZMPUytWADKHslMy2baPp9aKVMeR6\nMID39vaKYjcAyXM0GAziRz/6Ubx69arQvecGgDkc3p339YMf/CA++uijkkLl1D48nDb2/WxAl3mb\nNC7mjfU+LlZaEXdA8cmTJ3F9fR2vX78ufAEf5ugcdIWBCU2xxqtt27i4uChrKfCW5ui0DWUMq4i1\nUrB8gU7xOHsHQu7x3Nzc3JQNb/jPa6f39vbi+Pi4E916+vRpcXAwF06XdPTTQMbOIo4ZIS3WRq0j\ninYY2MAGkFu5RXR31WOjhdvb2/j4448Lr/b7/ZK2xQYV3jgm0ytpWicnJ7G3txc//vGPi1yEh7if\nuaNtyAgrd+SEoxOMOzrGUbfBYBCnp6cdGWp5TLn73ETEZkcSAIbrc6aA9Y6djhFRInkYMZY58FwG\nBtCMwaZlgGmNcbExCR3s7OyUXWsNfuA7RzL4jfbYIeooGu1i2QPRDCIbX3zxRbx8+bIAw6urq855\nrjX9lR0JdrhiVJOtwH18znrc42r54vRvAyL6BxjDDsnzAy/Bc0SkMPLyzqN2XBgQM6eOqNihjKy6\nubkpcns+n8fFxUU0TVOcArQPPQUfIK/M21xPP1+9ehWDwaCAVsv+Z8+excHBQXFeZVAFHZiOvbaU\nPubrfH1+J3JlnnvMUAb4AqzRfYyrswmOj4/LLqx2YrHB0k9+8pN4/fp1rFarzuZVBmXYqzj3cjQT\ncMV4WBYTcfvFL34Rp6encXh4WHa6ZT6Rzzga7FQATLCOk6UNf/qnf1qOQ3F0H/nBjsgRUZZpeNyt\nK3G+en1tBj1EDKErjyf2Kc5cgLVlFrrc0X7vMG1bxBmHBmSWT5nXkGfI0V6vV+TUfD6Pn/3sZ7FY\nLOL58+cREUUrrKW4AAAgAElEQVS2MB6MN0ciWk5ga2SHgx3FfkH7drTj6Pf4O0rpd//Oem5sWMA8\n9hwOCGcB2fn/TcoHATJtyPi3g4ODODk5iZ/97Gdxe3sbz54960QXd3d34/j4ONq2LWsqzZAYjfkA\nYXvwMGhIUQEUZCPeXspsjDvK6HvcFzwxBjeOlrJTLNflqAGKHC9NVt7ZY5+fb0avCV0DJIS6BQQK\njOdmA5z5M+O4GFzYCHQUKIM8BCJC7/b2tkQV2GYeAeIz4wxMeF60bdnoh/oNSDMosoE6HA7LLm6z\n2awAM+bVjgVHH2z0GWjYWOW758hOFhs8mK/Qhu+jvfYS0x/m1YXsAFKwI9YACEE+HA7j5OQkPv74\n43Iws41i6Jl73HY7S/KaETzljIHXT2SjjLk8PDyMg4ODeP36dZyennZ4grZnkAFdAPQ4r4trzs/P\ny458KDzzDUY39WbD2nOKcqXdKBIiqMgfr5VAibF+kRTTiCgRIwxP6M1rXmyA0XYDTIAXY8W1doIg\nd+hTjkoy3xifTou24WePtxXfYrEoDoFPPvmkGGy9Xq9EnzAgkKHwLnRGO7z2jXnhfFjL6Cz/6DsG\nA3VaX9gZRFQKQ4z+vn79OnKpKd+27WTil8L8M4c2ZvKY2kgyXeIUsGff82WahIaRMwBmRxYYC2QZ\nz+S4kl6v10nRyzIrIjrRFJ5hnW4nImNNhoejY9B7RMTPf/7zePXqVUnjfvXqVeETxgqeNLBmPrMj\nII8rz6U+aLtpmtJvZJZBV6Z3O2IMAqFDz43TaeFPdgDF2LOOsGPIYNl1Z4ee77djCzniqBH0DR2y\nhKjqEGvX8tB6fDhc7ziLU81r8zlHkrMbfQRUplv0sTcCzP/bzvC4AKqdVsk4PQQyAZistzStNs36\nCBN2mic6aVBAf9nV1bRZ2+3c+p1IErRjG9W62zqHcVqtVmUpxEcffVSyRiKirFO2jRVxJ1PJ1Oj3\n75ab4Mhhl1qvX7Qt53k13VsmZF5fLpfFboB2DLYsl+FZAL2P0YIOjo+PS7o8cp0010w35gU7GS0X\nzK+mF/ft5uYmjo+Py3zx+vzzzyMi4smTJx2HAW3G7vBGZTgZiJR6jGk79GU7zEuSsi1v+ROxzi7h\nRUaFP/MsYxLk8nA4LLxEJN8bx33d8kGATAY2G/xObyO15vnz5/HFF18UY4XUmQwqTGAQuMFRxDo9\nDWL1luwZTBpEuI0o54joGGhWMEQbIrrr7BBCAFwbg45ieT2YhSZ9wwtBaiegwQICEILi5H5K9iii\nQBwd8pjWvC4uGWxaIVO/DSvPP6CfcbGwJQWQNA8iMhjtzJ/BisGalZTnkH5aaaFMMYLPz8/L2C0W\ni84C9n6/X9aOolgMTlj34vGxQKbfNpQZd19nkBCxBsqOepCW4nHzXFMP66vY5c1KjIOHnz17VoCr\nlQSA0YDW8+znMgdc61RBADxz5PQN5gBD6LPPPutslmDFbSOPcSM62uv1ivcPI+b169fxi1/8ongg\n7bihH9Bb9uxmQyeiG8HzeNsbi4L3hmTIJuQW/bYHGzqKWO+eaqcWYCGn31EfDrnV6i7d9+LioqQE\nO0ILiKUfdsJkMOvNXLzFPrxjpdTr3aUZff7559G2bXz88cdFAZM6C1+hhA1GGZP5fF7Sl5jTyWTS\nMVbwvmYHgHnJzj3zwt7eXlxeXnau4Tk4OF+9elUcSI5om4/v3iMiujzHuNmQzylfBjxez8ZzkG3e\njdb9NL/ZcGGuLaeyLmLeWRN5dXVV1s8ZwMAXy+Wy7GAJXxuM2dmX5YMNJsAya6K/+OKLotPfvHkT\nl5eXnSg8bbDMsiy3Qck8+RrkG99xolimo1egT5w+EWsjzn2zDrSeo27q9/pF+uClNhjcZA044plt\nJNOv2wAdZcDUtm3he3iOFGgv16ktn4CUeTY8iQ4gbRm69Bpd1tC7b8hLOyX5HcM605Ftk6xLGbuc\nDYOMglddiJDj+AQgZwcJ19WcJ8wv40jWFXYb+pi5dnYBbcjzZ9uI+0zvpjWnqV9cXJT5wJnGJncG\nCMgWolbIHviS9lLQK7THWSQuyBJo12AoyyLm3fNiHiCAxFjiVHNqLnqLNrp+0zzjl7M7LCN8DX3j\n+3Q67azXJgo4nU4L0Dw6Ouos/4DHkJ3eSTgiSj9wvlC3nfhO8zXtA6axdWgvOpLfnZFl2YLcN69w\nDeNI5Bo7krkyiM6yZVP5IEBmxP3NPiC46XQak8kkLi4u4vT0NH74wx+W7Z/39/c7BgZEBuEwWRFr\nIjTRM+g28G3AZqaHsLMXHCMOpWAh6ghjxHptAkSK0EdoOEWU680MgEXC5vnQ6+FwWDZCoTiFAqXm\n/xB+WQBYOBic+92echsiMFMWSGYYKxkKERYMMfrP9Sj3tr2LXuNFh0GyA8HPsNDLtOaUSzxoXpvB\nOUqkABFRZUwPDw9jMpmUCAApJrQ3g8MsoG3I17zwEWtjlt881jY8iJBlj56LDWvWY/G88XhcopeA\nIhu+/f56Y4HVan2AunfkNBByhBf6hyZt/EPHCNuI9RrTTz/9tJxfSZ/gUc+vS+aNXq8Xr1+/ji+/\n/DJevHhRgFVNYRpoMT4R0dkcwwYf93jOmHfmHD7DMw4wbJqmjCVjxbUZvGXvbkR0lK6NEztN2NCG\ndWxOL8U5xvxaLppHUNCWr7Qrzy3HJEDLRG++/PLLaNs2fvjDH5bUWdaO2XBjTG0ceQ0vRwFhQE6n\n042GhOfGDrxswC4WixiNRrFarcou3j6K5vj4OPr9frx8+bJzFAdjcp/P7i8jMDA0jSGDahv8+N3O\nBwxJy3PXB315F1+nxgL0snHFcyaTSaF9O17JOhiNRiWTyGvOvH7eTjPeaTP0TjtxAEFTrB+07qEN\npotszNq5YdnJukrADynbBousp3PEAEcT4MogozPbX9EjbXDUgzr4nbHmOTniDMA/PDyMnZ2dODs7\n68gR5Joj0QbT1GnHF/PMMUHOEHH0JO8pkevM0UJAGnKFftrRNJlMir1ix5153HqNttgJbZvLgN5R\nVWepMZaTyaScZ02fiFCSRkwkE54w75tPPJ4Gf05J5jpkn3djzfOObVgDRxH3z0a1jDw4OIjxeFx4\nHP5BljAv1u3o79lsVs7qZIwAQ4Bj8y3zRjs8H9Z1dtBBmxTkL/djJ2Ud53Xh6DbLaPgYWW/72g4m\nvuPYMW2abpCNzAeZDrSXPlMvDmscQF9++WUsFnenB0wmk8IHmY6ZG8898+PNzKCBjEOs20wHpEGT\nDcG7+ddOGjuPbSPiTGuapjggsMWxHdDXDuLQt03lgwGZNgwykMMbhueAbe3Pz887XmsbihZMXj9n\n8GQQY69ITYhkg8vvEWvi5tlO9YJY8R6zDssAk3cW1EMkCHQMQI4EMPGZ4SA6gGg2OrOiMpD0O0a5\nidrFSmt+exvRdiNuzKmFv4ERggZjBCaBQXmm20UbvI6IdDm8sV4nQ/t9P/OWgTvtQPhAG04XhMFO\nTk6i3++XHdjo35s3b4qxxRxmhWGBmhUP/9/zIqfxNw2b1m1UZSM2FzxZKFZokM1VbGihhMfjcQGe\nODvweqOIoWOvnaA/tMOKyNFgK+C2vduM5OrqKs7OzkraKYrU3nZAiV/e8ZK5ffnyZUm7c7TP7XH0\nhTG0YejfcSxZVvHOtZZrzCFCGz5zBJLx8Xoqz202PlgfQ4QKL6cNWgAmmQzQE3SfD46Hb3OEzfeY\nn7NDyWMKaEPBz+fzePPmTVnni4PIW/ND2wACaAQD5Msvv4xPP/00nj17FvH/U/f2MLJsWb7Xisj6\nOFVZlXXqfPW9UveoNZrRzFg8Y4SFAXoCCQeE8wQGIEAMDh4GDwwMnvOEQJiIwcIBPZwRCCEEQsJn\nhIUBI4Fm1HO7+95zTlVlZmVlfUUGRtZvxy9W7jy35023dDukUlVlRuzYe+318V8fe+8YDiiHhpZf\nxso4DIToMzSFb1+/fl32AeD8O/To6elpvH37Nq6urkYHksNLluvaRabSwIZ2qRiwTOe2PAbeyTzl\n7+3UWqdkm2NwRR+9mZmBGECTc+MAcdDawVzGYR2IXNuGbjabUrYPPQjGwDM5Ip91BZftnOmDfHCe\nMNlMSnwJBj49PcVyuSzr1SzXnl/0jDMDln3kDB3g7DV6AHpmxxXgSRCG4ybsSEQMQRfrlpoj5n7Z\nruFUcCYg42L3YY+TcSPHZJas71k7jrMLP1F91Pd9CWyaLmABzyH9Zmx27pq2jUnbRkg/OxHgi+oR\nAr7wpPcVINCaA6mMBd3JngDGdp5HaOIsHjYCx8lOE3YzY1/GD95r27bsItu2w27dbBQF72KPCR5w\nBirBDypDOKYJxwG8dX5+HhcXF+WkAG9sSNWgdWV2vuwIGbdyP3PlgLuDTg6O24F01tF7ssBn1ifW\nD1QJ0h/+9/Ic40roBN/BO+gOnHkyrTjyXbfdnK7runj//n2hu/0BcCQl6wTwcO5t99Ch1ne2Zdk2\n5Owi78pY33baPhDtUjINH5Ilf35+jp/97Gfx13/91/Hp06ey03fXDdWDvxVOZsS4DK3v+6KMlstl\nAa0MkK24I8brHWgnZ3+4agDA2QdPTH7OTgHvRck70pJBDN+zBgMmt5Dywz1MoMELjO/yC6LfRGW9\nkJxx8Lej3qYz/9vo2bmqAR1/1kRE8zJmQAfvQyAcTeOyEaKunmib58ZRPGjse9jFiyyAs6A2zpk3\n8v9WWtAMA+aF81574cifI5K05fGOonp9X4rp4B3zIUazRnvTzxEpR8m4l3az8cVJwknpum3pGzvq\n0R5Zt3fv3hWgjgFx0AZepByQ+cIhdGYAUEtEkCjebDYr8xURcX19HVdXVyOHFKcIB8xrlBkjRgEA\nQUDg6uqqyAZAwNkxHLMcfInYPWPRPF3L1KJL+n5cQWAdB2igHJgx+d2AFwwgc+kAFxHhphkOkqbc\nfL1eF/AeMQR3XAJpIB8RhTbOOAMKmmYolWOslnODUsaPUUX/+PB5AP9msynz7gAFII+gHMZ0MpnE\nL3/5y/j6669LEIFzNAmAOJCGw8VcWbdZ97FOBoN7fHwcnz59GjkDgAPG51KtLzmYpmnEoJun02kB\nG/CWA0b5ssMG4LHcW/YZt8Gg1/UayPgcO/iK7wCyb9++LboXO4Us5JI1AJWDTQ4Q0j8A9HK5LEsh\nANLPz88FFGfQZXkzqHKp2cHBQUyn0xI0xXZ23bChi/mX79njAVBvJxPedjCYig9vpsF97jf6yfPo\n9XLIHrYG/YJzhkzYZtT+znqMd8G7zlJzpiVHnqA/jSMIBGGfaJ++G7M5m+vAG0F8aA5fgN283MgO\n5o4cDTcV3iTQ5qw+sgRQxqlgnsk82q7AM5SBezwPDw9xfX0dEVFK9ukfOgzH0Xr++fm5OIVgK5YY\nMG/oK96PXaSdvDssG2DB/2TqevEV56c6GIqTRbC1aZoSCAS7oZNIeGw2m2KbcUy9C2l2pnAEcU45\nexNn0GuecTKRCQfesPlnZ2dlR1rzM+3QR/MPDiG79BIosCw6gGbn2YHSyWQSb968KXqUwANOIXqk\nbdtS3fb+/ftSOouuAuvwP0EAeIaNv+wLYG/N2xnPYdtrgTC344A6MmM/ht2VLy8vC/8z/33fx09+\n8pP46quvYj6fx6dPn2KxWJSzrDebTZGL2vWDcDJtEB2dnM/nhVkuLy/j9evXJYIM0MeLt+HOCg7l\ny5WjBFkR85mBvy9Hbtz/iEGB8g76BfjDCczMZGBpwFyLZtPHCG1sE8MGK9DDStDGGQHOTrlruImA\n2bBynw06SorLAM7K09Fnl0rmTDQAhbHzTjvWAG9HoHwsApHL7GiW+zVnk8kkWpVsAIbKdy9KEUXI\nzousCZ3NZmUeoGemGWMqgQ+BRN7JlaPd8AXzlnnVc0yf6b+VTjbWAATvFMd6Dit3jo74/PlzARl2\nENktkD7Cdzg5VuKeY3iCSLp3Njw6OhpFjzMt4TGAAdF16IGRBpQtl8v49OlTcSAoNbJcGEjTXwMg\n5AXH2fMGzTBADhARQeZvzxPvt0zZqYSvkStKWQ0m4RFkzWfpYVRojz57g5NaJsZG3EcTYVCtU5Ev\nfjCIvIfvKU1zBhQQ5OoM5Bj+wQGBj1gzhVPw3Xffxbt372I6nZYgJLKSZcqZjizfVJewEVLf9wUk\nYYAdqUdurH8NqGsXus/lsgB+eNvOpe2AL8t/xPhM6Xxfnn/zLRflzQ6uOdgJ3eERl2blICn9hrcd\nuECv0A7ywhnQ7PKMPcTZwSEhkOqgISCVzCM2xcEfl6H5uc1mU/jSNhy+QAYc1OTddtopPaTSItv0\nTHPkmO9cPmoQSzWS34VuMa9k/EQFgJ15nBdklDagP21hy5Ez83zOqKBzHVBnB+Saw+eAFnYE3YmO\n8NyYp7Jd5H3GfozP12QyiYuLixKIA1tgO5BjfqAdRxg5iIZOodT43bt3Rd8hO3bm0dEEC1jni+3i\ns8PDw6LnHRxBpunLw8NDfPPNNyVrBp86k9W+OKRkLOlb3/fFQfz8+XPc3d0VPAFNmWtjRIINbE7l\nM19rPJgDoPAEtMcmY3Oenp7KkjjPq+kILTOW8A9y44oY8DZVQU4gedwZdzujGrHF7ldXV9G2bVnD\na9mwPFKKTsULTiBzjm1D7ggkWN84+ML4wb840uje6XQ60hlka3mHs6I4xtZPyB6BYJeMX19fl0w3\nct33fVl/io49PDyMv/iLv4h91w/CyeQyuCPCSSSByfjw4UPJjMD0jk4D9g1qI2LENFbaEbvnge1j\n4mzEYYKIod7czg+MAZDOZ8LZEDEGOzlcNhguKTJYyxEZGzgr6zze/Bl9ur29LSVLNmAGQTAZdHaE\n2XOZHXX6w7gdoeF7O/oG1tDUhh/ha9u2rNNkfSzGjDaapimZVxuOph2v+XAmFyWJsHPGFGvJOFIF\nEM5c5uBB4cfNOEuendI+8pYh2wta8reBkTNo5sc8z34Pht3bg2OoMQar1aqUJPO9S+KaZrwtPXNu\nwOo1KVbkPjy8bduS5f/48WNEbMt38poM6AodUPoADoAjmbrb29tyvq6jzDh09MuyDG2dpSFgBE39\nP7+huQGlHWeDa4wT2Q+vT7GzgjEx6LPz4SwGxqU25zhNRNDzhkQRUdZrMl/wCHyVgxnMM/3ke+aW\nEms71BFRQD1ldEROmVuCAcyp38244dGu62I+n5fSMowjfOj5NG35cUkxdoXABeX7PI8xZq5yKapt\nzj5H07rBZ+nZQTCd/ZPl1/rS48q2L2K822QOonjNGfTwbufwvIOQyE/W7e6vwU3TNKViwToRAOtA\nArJHe+gesm8ODNkpdjWM1ycCwgzYKJd0phXaoAesJ9A18Do0jYiRzNGOHXvkAflE5+Fc22mlr/AK\ndg05RxcYdMN7vBsHhDHa4UEeoBGy9Pz8XCorvJbda9lqfAj/WfaRdeaHOfS80mecTPO8v8/BgZpj\nwI/tn+VtNpuV4BW8mANjOJjoFTAEbfKD3JNdz0e3QFuwgKufwFPMAZgGewXvole9Uy+60k4LfA+d\n27Yt+ha+AftQxUPihqSC9xhwpRtZQPPf5eVlCeTd3d0VnjD/Zfn33CHv9IlgEM6mAwS0R2Afp9y2\nqIZhyViTcDHWNMZ1gsVObcYp3h8CmoD52HkcHMsz2NeLi4s4Pz/fCZwdHByUKqPNZrv0gGPWsH3G\nCeAXaJ6zx/6BFx3QhgbGpMaL9Nu8bmxEP9BfjAe7bfxau34QTmY2iBHj0iKAmBdjkwZnQhg0pUAI\nu5VcjRj7wIAVowELbThCmjMGdviYFCtrg1Er11zy6wyDlTztkF2hjKxpmqKYzKj021FIRwqhFQov\nGzrmImJQjignGNAgZR89PRbAd1bMOIUYSztoLjeyomGsBBhMQ34AjjwH2PM9zqBGRDEMABxvCITQ\nEX1/8+ZNeYfLSnPZbP7bnxVelCz4HoPl/D39tgNi3q/Nh9csMifQAINGlgOnk/YxUtzjoAfygoLG\n6FmmMMCAbEDJ58+fy73X19eF9gBUZBwwbFBHaRN9paSDLDaGyudUWRk7A+9gleUE42UHqO/7ajDG\n8wy/4exBPwAoTquNC4bBQJ7xAow8x55DaIwuwrCTIbHuIVjn9VGMl+i7+Qq97EyTnS76StUGu8Aa\ngDKHNzc3cXd3V0pTM4+alnaMaC8DjsvLyzg6Ooqbm5siJ8i2x8C8EjBC7zDX0AY+84/7wjOm25ec\nTDusONv+3OP+0k/EeJdN/jY9IoayfPgfHss6sOu6YqfYKTQiytzv6xe0zDrNvApvIufu/2Kx2NlE\nyf0HVPEOZ8ehN30nkGv7x73oOQAjfckBH/QScge/EVAkqAK/YLORY/psu2WQa7rAB9YxBsPQ2M4g\n+io7mZZL61YHPWvA3PNHgAds4yu/r+YMuhTZ/JLn1nRxYBb9Cx/XaJH77Cy1nST3m6NVnPXE0fec\n43ByP/fZuYSWk8l2DSSOZg6yImdgIzZC48qBdHQtOhVeI+PlZR45aAdtsLfGbiRjrq+vi0403gKH\n+TPWrzLfJUvatiXQc3JyUuyG77WDk+fNPAh+yv/TZ+6PiBL8M08Ykxq3ZF0FXbNNr+ksnjHG91mU\nzKlxP5WL4AGyfjc3NzGZTOLDhw+jsmPmyXJtG8leAJ8+fYr5fF6Cn7UgC7+N5SeTSSnzdhkyY89B\nHvMfcm/Mf3x8XI7MwpcBB2Wcs+/6WzuZTdP8ZUQsI6KLiOe+7/+4aZo3EfGPIuKnEfGXEfH3+r7f\nX7Qb41I4D77v+/j2228LUezB23Fy6QYRRjOeDTLt5yhsNE0xyFzZUbWTkj+LGKJ6TLoXHNuJ4Z7M\n4FyOVhgMO7uGcOeoBeA796UGsgw2UIj8humyc2Oj73Y8juxU0Qeyuo7y2cABth0dsvNtIUGIoANC\n5KgY4zUgdXQ+8xW8hEGAjoCuvu9LJIvIliNzudSLqBDjN03oA79LRiLJhWldc1b537xFX2v38hmR\nfBwJl1egdAwGzDtclPlExGhXTBtbQJojhpPJpBxqjTNCRt5g/+npqTjyR0dHZe0Tcm7AizE/Ojoq\ngZeDg4MCAljXx702DPAQgNhgGDDT9/3IyfB4HFAAwDgr5bkGkLZtW8pQKD02iMYxQPGzpoO5s8xZ\nZ0YMVRTZABKpx+iw9oxIasRgtHPQrga47Jgb9Ft2+CEIwHu5h7UrnDdGUBF6Wb+0bVvWizjAgJyt\n1+tylqazY54zHG10kemW5cwACCfTtsZZtRzcrMmcgwRZZu18OCvAsw6K8VyWR561bbN8ZGcWfvc6\nKwIO7hPv4sp2sQbYTFccP+a067ryTtOvRkf3mXc7UIu84DQwRw62eJ2pARlz4XmAVpvNsKGZSx55\nN/oFmbWj4WArGSPLhUE+sk3fqSigfeQYfYx8e26RC1ejGGTagTUfGOSj49GRPGM77TnlsmPhoES+\nLFteS+ZgROZP61jLVN/3Zd78feZTgp0ODENzO+R2MqmIsN7jB5oiv5TOetmIA4pZb6GnWH9ufnbJ\nYtM0xYnLOC1npCKGajfrJvpze3sbV1dXpToNeYRvwV44EMaT8IoxFrTn/FywaK7cciCIfppfkXeC\n1RGxMzZ40/jaWNb4xAFVeMzOE067+QzecVY2O6jGjcaRBMmookHHgXNvbm7i6Ogo3r9/X86gpY9s\n+IceJPCLXFuX+O8iB9sPRnJlzEa1jXkwO+qWXe7FLsIjlhNXJhib54BUvn5dmcx/pu/7T/r/70fE\n/9b3/T9smubvv/z/7+972ErdigIBcEYrYpgAno0YwEDEEKVy5CIbKf8ubcVL5DdFNGrMR/9G/Wia\nCAmZ16X5/hx5oU0AUNM0xclB4CMGY+VdsyKirB3IoCAre/pBFpT/UbrZOTDYdbYyzx20dKYCQ8tY\nifoRtUa50Bf6ZUNAHzwe5hJasUOqzx4r5WyHh6OgAWM2oOn7vpSn1gIPCCTOQI5QsiaTDams+FDk\n/J/X/NlIwB+ZNzMvlrns+8Jr5hMbiS9ddjIBJPCCgQpOTuZ1g11oHzGsSeZ+R4ydZWMtGhG+rutG\nZZSWWeiNIrbxcNmbQSYbiLiUlTnzOjD4FDpm483Fc54XR8UNoOB7H05ugICyx7EswQUBCWTPm3F4\nzRd9ZMw4BdnJBbQ6Ouu1IUdH22MSHASxDvB8O0sJT6KTXGlh3YOcexOE2WxWnER4bb1ex2KxiLOz\nswIUmC/LQ9u2pcya/iBXBnCUUKNPaQewj8OIbsk2pmaIc6bJDrRLswzea3JXM8x22rKdyQ5o/o65\nyA6q+cQOh2XXDqazadkJyc56DjzYxhnIYBesB3HuMz2z4xoxZO/om4ONnp+sk+yAQwtAHW0gP3b4\n7NjSBjoffuFZ+MggkHfVAlbuk/nL1U04Lzh71seWa7IhvD9nIAwos9OeedO4hPlh8yVjs+zwWS4J\nuOxzMDOvuz3e6zHQL/cz993yVpM1nqFsn3mHHq6MgWZsuJMrkHzBG4xhsVjE8fFxOZPSvJPl2uXB\ndnhc7s/ZiS5Tph3sMZgNumFfmS/GdH19Hb/85S9LZZIxH44uvOrgiTOK6A9jVpfS0m942Tvr20kx\nHdAL0NHy7YCveQ55Mh/ZDhsvuFLJgT0cZPNOxFDenek8m81GfSZY6+AImUaf4bnZbBNB19fXcXx8\nXCrd7A/0fV/2HEAfUKp9eXkZZ2dncXNzE/P5fCdIb/7O2N86KOtl5jU/B629mVM+emefjv8+mf9N\nlcv+ixHxT7/8/V9HxP8eX3AyuewdW4gjxmsOLHRWSBDX5S+AQ0dZ/IwNyz6CeSL8TvrFPY4sMGGM\ny5FAG0czDbX7Bgh2rCmbgBaMzXXzGTRlsLHPEGcnxoxq2tgIZ+PpeaLPNlwoTCtpO5qeb0emaAvD\n6vnAoXQtOjSZTCbRJGfB8+MgwiY5mDai8FPEViGzdoV+Ef13+4AaSrVywMNK3fMVEZHNmo1qdvYy\nz2Kw8lCJzokAACAASURBVHxG4mvuc7TKMpYdIuQpO0weC9lGZ3EdDedeNuUhi8T9vJ+MZg5qlPns\nhw0kcmaJzWIwoPCI6QFP4rB4buHR7HhGxGjTAu6PiFFZqmlHdtDRRXjCINA0hAbwD+OwEaR8yOCW\nMfLew8PDUsrk+UXG0E/O5LpKwO070mnHAdBrxx3wQn9yKdXBwUGZXxxBZJpdLin7whG2HDgqDZiw\n7kNevcaF9j2nzLvBjHnKcpfBDp9BSxxinOgvOZoO4OXPa87k2HFvo213M3s45fucTAMK7ifr4bmy\no5X7V7OL1l/ZOfOcu+Lg4eEhbm5uRhtKGXRl8FMDkKZrniODQGjAGL120rbSu8Giq+Ep21rGDP1s\nP7KTa/th+aOPdrIs2/wdMaz5tF4G/FINkfdoyNjE9PHf+5xyZJi2kFGcDOyqsQvPWpbdh8xD9IVM\nLTShTNT6uBYo8caJeXw1eSMQjZyiJ53txZGn6iWPxbigacbl909P22NvOGYry1mWS37DE94n4Pr6\nuuzKnIM3zoQz5w4aogd4593dXXz33Xcl4Irz9Pj4WGSQ4CW8hi31sqCI8RIJvz8nFsA7jN9y5s9o\nk+oW5r7ruhK0fn5+LrjB9s3BKW8M5gCU9YftfbbFHpfvpX9kVh1MNG87cE1wgKzmZrPdNfbTp0/F\nYSV4xKZqOehFgOfs7CzOzs7i/fv3MZvNyqZNOWm1Ty9D60x3O/yWScbLRb+y4w2+sPyZJrXr1+Fk\n9hHxvzRN00fEf9n3/Z9GxI/6vv/Fy/e/jIgf5YeapvmTiPiTiCib+jhiFzEsas6GJBtiO542/rTn\nNLbr4msOhQU7OzRZMWellg2bnSQ7uPztCQRQGcDytx1t9xfGgVkdqXbb/A2TOdpmRyFirMAMlrjM\n2BZmv4/+WxHyt42Bx1eL/nnMRMhQpM6+AUQpRwHcPj4+FsfUdHNdvgFOhUcjmm1pAvTdbIbSWQM3\n/vcFTRx99LoQ+JR5KaVGfT9yCmvAlQxtNn7QJyufl//eNU3z5xERb9++3TGw0AOZ8doeonSWUc9Z\nDXBBH0eOWfPikry845wXv3su2hcH0/9b9ikzIwPgOcYJ9jbhzHt2PJgzgyc7Exg7j9UBIvNF1l/W\nEZ5X6BkxHCFSxhARTT+UYXkHRzvv6FDaQL4PDw9LVYXXlNrRjIgdPoCnyeSgB218rHsODw/LLq3e\nUMJzBICjDMw7qzZNU8rPDZ5MOwNDgwzG7cyRM7/IHt9hKMnCZp2Yecv8xDj8OX3Izrpl7v379yOH\nxfKdf2rgPKKPvh9XXFgnQA+Xm3NvzmKydshnNUOj3ffGzmcOAjvY4f5bt+AczOfzkgUhs4I+tlNn\nuctODb9tX61/sKU8j91n7gGTyAVjd9l113WjIwcsW9nuIh9cGdTaljVNs7M/gx1hf0af0OmMw3KH\n7XPgj2ey45npxwW94BWeJegDvQgE2pbDWzUMsu/ynFkPEnjwZmHZRtfsdOWzInOXl5claGX9jQNi\n/qT80/jDvw3abQPAP+v1ugT4eM462nzd9/1oPwLOpfS+EQ5Amy+si81fDravVqtyTAn3bTabWC6X\n5ci3iGGZBxVBtmvoAtsadLLLTx2kss4HL2D7+Zu+cBEw4TfBUfbA8BpT5pDnrHeNkZBX21zkH5nm\ne+t7B5NyYM722rgLHiVgtdls4vT0tNiY+XxeZAyaMacEQcBC6AL0JDuvX15elvM3sdu2RzxLP7g8\nLmfRbTssQw7s2TeBD/mfhJfnZ9/163Ay/6m+779pmuZDRPyvTdP83/6y7/u+2TqgkT7/04j404iI\nN2/e9HYeKOHxeq8s7Pl/R7Ve2h8BQitcEyULRnasbMANOniH+xARBcyi/HmG8qicNbDSwgH2O3Pk\nxEJhp5hSTYyRn80ZCgOBOpgZR4zzuN0//0/WxN8Z/EAbg3+DwAye/DzzZkEkOOFIKzT1/Genjjk3\nz+Sxl79FewuwDYF5IRs8FAICDqiyg911XTHgFmTPQ+b7zOv+nMgo/dW9n/q+/+OIiN/93d/tUfwG\n7QaH7HrnDSS4lx94CgNiusP3zA/OqjdQYS6cGXCEtCaLXChExssh0j5PEV4BkKHMuTwP0MtZUOYX\n5ezy1ogY0c5g3bzk93GfyzW5bGydaWes/Da4Yew5WERfu64ru/aaLsgr/EKE03zIO9FZPOcjXDgH\n1dkVdtQDuPKsNwZpmqaUCbF9PfJBthd9Zh3BeA2wuq4rO5FT3vT4+FjW/GLQ6beDXsy31+TkubMx\nz3rNOsYZTc1dkbnf//3f77d6o42twziuLvHfmXdsg7JNgP98n2WLzw2I7+7uyrmi1o92IvZdmRbQ\nNINz292u60ZHlGADcTb7vi9ZLHRXLtEzAPS4HPAA9HrnSgMo2uI9AD3uMa8DQglWOgPNu+BP0zjP\nl7Mn2eGlfTIbOThWq/7hHjIojN0VB/QhOyzmbd5BfyKG8mT6u1qt4uLiYqRvctkweqB27bOr9I85\nYmxUaWDbs51DrpwMqFxF5n7605/2Dpgin4zHjjkZKJZj0Gffxxj8PboPXvZGOjlg1Pd9WfOKDmRN\nox0jj9MljBnjeB6w5eyovlqtSpur1Spubm5Gts2BJWMqeN1OiYMXxlQOBme7Cq0jYiRT3k/AfE4y\niN8sDXt6ehqtjXQFlhNSxg/meWiDXaCvBEdsD6EX+hx8YVl0UIWxZr9isViU5VwEEe7u7uLdu3ej\nXbYd1LEDS9ACW8gRTU3TjHSF9Wvmh8zf9JX7HICyTEAfaEZAkv6y1Ih58TFztetv7WT2ff/Ny+/v\nmqb5s4j4JyPi26Zpvu77/hdN03wdEd99XztEYNkAhPJDR/hNgAy2awCB713jDcO1bTuKKNiZ4V1W\ncvbuuY+1fEwOijlnMe2AErFkDHaQsqNs5zk7GpPJsBkB99lg2KBgkExL+oPgZ2DjMeXLUSODkto6\nBtOPCKUztqaPHe4cjYkYZzxxHBiX1/PlDA/jdLlcxEtZatPsHBdSeCdi57ssoDbkNVrRbxxNb0zB\n2MiAeSG+I/P75sH3+PIaysxTvpxpMH9PJsO26jY4/Fhx28E0jeALb6rAZklkglkMD2hBHh0lzyAl\ny6jLipwNgOZe/4XDBc9HRAGZ/E35Du+AFoyLIA48DG25x+teeMYRaCtyO1N5rg3iWz3DuK0H7Gwh\nI7zXm45l54H7XD2SS/kAmtxP5JI+uLSU6Ca75NE+8+GAjB3Nruvi/Py8BBrsFDDnBgCma95ULSIK\nKKYt5sxZtxotnUUwnTwfdgIYh3U4tNwX3d22HxExXnqQbVsOTOTPuNd/GzAYqPqezWZTHEzziuUt\nB86y41QLpDB+2suBXXaRdfac93Gcx3K5HK0Dt16Bv+FR2/Ts/EeMdyDPenKf8w7+wD6zEVeWK/rg\nufJcmo/t6NohZjx2IOg33+Mwe5OfLAs5u8WFjmKcgGZjAICzM0XO4PR9X6o/OMcYwA0v+3e+6FPN\nufTf2YaS8XFWjXvNe6b7Phtnp8F6zvYLHX9xcTHi+axjc2YYnnE/OeLCjhT0zTzy/DxsvGZHwdUV\nnqecLbTuYHzr9Trm83lxZCMibm5uykkMEUNANdskdGMelwPz8IirTaw/c5KgZsO7ritOVqYlvE3Z\nOn/zLpZBOFtv58ryyBjNMw7U7MPbyEB2VvPfzAvj5J3QisAnx3Pd3NxExLaKDEfTZcAOStBf5LPr\nunIG/Pn5+WivBi4CvnZcLTeM33xsOxkxVOPxPPoQWqDPLFe/0Y1/mqaZRkTb9/3y5e9/LiL+44j4\nHyLiX4+If/jy+7//nnZKKhgBiaaJVsxp4YYRrPTNXFw2PI48WPk5w8REjABeMsK+xw5mRIycBzMk\nGRQLlUGXJ9pRSvpgY5wdOzM8TA8j2QnyjxVoDbxEGpeNc02Z2+B5jqAbjAugRsl5kwP6mSOxzIHn\nmrbJhkVsBSwrdqKLLmlydN+jtiEr/4sPskHLij63nXmS/jKPOTtEVsdbl7u02+0U/kvzAp8j+Hlt\nWW3ObGhp4/DwsJQY0RZG3xEynEfzVFbuNtCAFkB4rWzF/Jp5wPQmmup5hWZdNxxp4JIOPsepp5wF\nWXKk0nLhCK4BH7+hizce4rJMR4yjrBFDRoSIp9c85fI8G1b+hw9ywMg7d3pMtGf5JJJOfwH30IpM\nk50nR0aR6Ygo65PY6RIeyO91RN5RbtPX9DcggqbmIe5lTe7r169L/xg/wCb/0L4DHJ4/srsGEIyB\neUDf5PItO7+2CQbWGdBk/WF+q8lxvt9ALwNJb75mYF8L+mXbUANl7rfv9dhwar2ZSe5X2w6bNrG2\n2wEfA14yf7Yt6AM7UTxnfWQeQhfDG8wbc8xh9s6UMrY8Xo/FgMyOpe/1HNmu40TyHldY5Hcaz3hM\ntsH8DZg1nrEzkXGE9QWOE/d4DbrH7b6Yh7ITajsWwhR2qnL1D88Zg2T7+iU7h61ygN+2iWMfcM6M\nsexgZt7Kjgg27uzsrGA/6+C2bcvxH6w9bJpmtP7egTzb04xheS9ju7+/j8ViUdapPz09xfX1dSwW\ni8KDrvDBZhGQh9fZMZcAM3NNltEy3rbtaMmCEx/ZYTI2o//gBy8pQq5ZK4qMHxwM+3LYYYdfkZdc\nEQBPoeuzvrMdQNdnPWJd6j4ihw7YOCgQsQ08z2azODo6itVqVejGWHJSKttqLsqpZ7NZvH37dgcz\nEHQl8EoW2DgrZ+SzLJlPaQcZQofbdlAF9aXrb5vJ/FFE/NlLhw8i4r/p+/5/bprm/4iI/65pmn8r\nIv4qIv7elxphMm9ubsq5exH7a/AjhmgtTGMhz2VCXFaiZnS+c5mDmRDmsWJk6um7I6CeRECk37HZ\nDOsPAD52OJyGzwA+M4INSo5SONKYwQuf+76+367/ysLlZ6zcatFs+pKNjQGNd0czqKMN08NChhJk\nnM5kUkLgzWmYN4A3v2uOY6ZN/rvQRwY6j8uXx879ji7n8lh4ouu6sutoRBSHxfTOPO0LR+jg4KAs\n7G+aZmczIeiRnUAcXZfHevc9O5l28ugj8uNgB/22I7JYLAoPuLzXc8OYM5ixcbXhQqlSag+oRQaR\nEfo84vsXcECU1frh9va2PMfa1BpI5seOd805Maj1ehZomJ1tzxU6z7v+ci8AgHMO0TfIBc4khoj3\nGgRAEzJTOOmAPsCGeZf7s144PT0t/3udqUETc87h5hyv4rYA/9bpuWzMQI7xAfQwwqx9ye8377ps\nyEAC/t0XQHEmE/5xsKGmF2wrzNtZn2Re/VKb1gP+HH2Jc5Z5vxa0gi7MW35nLWDpOUEHL5fLkYPJ\nPc6QZJ18d3c3yvK53JH+M1fuO3KQMy+WZzsdACV0HjoCGma5hM+zTbbNz/o0A27eQYYGHQgNCALQ\nL0oJa/NKu8yR+5HnF0eG8wjZFRT9zv2Mm34DZAnKweuZlzMfZJ70fZ5zzz0XpYamY27nS//7c9u5\nHLiDXy4vL3cAN3Nu2+fvXNVjfGXsgg5wsCRnxtmfAGxkOYD/+NtOJnyD88wRJQSTrq+vi1PgwI7p\nQR98lBM/z8/PJQuHLrYtz/bP1UHGpc7U8T8yjF4+Pz8vPGy7nwMNDs54DglMWGZpzxUDuR92oJFJ\nKsrQX1knuP/+jMB2DgR4DwXs8M3NTdlxtmmGMl5X02R+hIfu7u6Kk8kF3vERbvAdv+04066DWP4e\nhxZ6ENgz76MLfqOZzL7v/7+I+Ccqn3+OiL/7N2mLDCYTbAWUjR6fZ4ay4u26bqcU0sDFjqSVM+/L\nkXqIyrt9ZUCBQjB4M3P6eBUUR9n05eX5nIUCUGalZhDgKEUGKRmI2Dhxz4jGFWOQ26oZ2uyEQQMH\nAwABuU2Phe9R5NDICiZHgL1T4vn5+U6wwPyTo2u1vz0fIz5s6tHTbED3tUekkR1WHWFGuImsRgyO\nJrTJzq7phmE4Pj6OxWIxBAxi97LDaMfGSjYbUj9jo2ADjBMEMEK5A/jn83kxhgDfzDvMnbNgmU/o\nnyN4OFg4E7nUB8fYjqffaQcsR9Ank0k5NJk+IcsZjJs3MKSWOSKANu6sicSpyoDN6048B3Y8c+ko\nIJL5wJE2Hznj0jRNLJfLMt7ValU2YKBkjrXXtI+xoS84A2waBcjx/HnXQPrAweboRR874NIi6Jx5\nwjJ8d3cX79+/33FMmIOa7GYQaXDE3OOw4lDyG5rwQ389F8yb+22dnXUqV81Z8VKNrKuyLucdlCZn\nGpj/vwTeretNr0xDP7dcLgv/MGcGbwAdzw9ye39/Pzq/Lx+5ZOee55kzHEaX8xno+eB6NiODV+Bp\nAJY3jvFYM50dAMvzDF2gvzNCBuTYefgJXQyPQUtjAPjTjo/1G/jEzgYVF9gi+IT2ciYIJxM+2WfD\nMz9msGw54F3mW+7B3vtICLeZ5Sq37ws+cZDUY764uCib0eU5y8FVf14rw2Usea0vF84Qut+b8PA9\nz2Qd7eQJOpSAp4+pur29jfl8PsKQDmzSL2TMTq/XPkYMG6lFbB0P70Jr59IZXT+XA0DMh20G55jC\n24yZ93K/+SjrN5x1ZMQb3Tmjh+7Jx7fQF5JcTTPsRo4tI4DppJNLhHPglP41TVMy5MwBR3a9efOm\n2EIHQK2js64F697e3pY1kdPptJwDzvMEIPLcZHm0roKXvIkhlUzYY/Ca5eBL12/qCJO/0bXZbEoa\nuaaorOysbKw0HeEoVyX6VWNUgzWMlZk93+d3OGODMje4QOEjcDCAHQzfj1IjosH/EeN1FS6dc+TM\nEU+DY347+2M6ms4G9zliyXMGYjxbi+ZZOdM2x1s4w2v6MKeOtEFDhNFZFYOviK1BvLu7K8AQZdA0\nzd4NBTw2/85X5skaP2UDm9tC+RN1ymtUn56e4ubmJmazWQHpOeiQ32f69n2/49TUxpENqEGKjYeN\naw1gmg78zW519JsNc25vb4tBsyJ1uaYdy6ys+W0Dj2O+Xq9jtVqVjF3OUpl+pik0N5/lYBZ09DzB\n//sqGOzA+rxMR2ehAaU6OchmmnvtVJ6PpmmKHqHc2QYwIkZggLFgNHA+0T0RMcp+dl1XDA+ZEIw7\nOi9nb9idkuCCAQ79MZh4ft5uAz+dTkdlwzxLP8zP5m/zpsvk6VfNGas5Ctnxwql0SSXzZ4czO5oG\nnvTPcmM+ttNsGtbG+fJBCQT6Hr+HNsmSMX++arrPdgP+N31rzhPfm68pzUUWeMZg2UAH3iNY5F1Y\nzX8G2KzzN00dkSfQ4bm3viOwk8sADw8Py86OlA3axnmerGdqYM785UyoASXOckQUwJ2zuN7RE750\nORx9wXkh4JT5wrYCUI7N8PpzZ3BwWr1WzhnNzEP7fuerhvW4eN++7Gx+Zw56ZH6AxxyQPz4+jvfv\n3+/IXM0hdV8NsjMuRFc7y8blXUSXy2Vx8j0OdKKzhnke0fFN04zOnL29vS2bzjB/0NBz7gofnBE7\nStgCl/ti161bcGYcDAFjOvMKzY3rnMhht/EsU8wR95nmORCMLUJ32DfITqplNM8n96J3sHdes41z\n7uUjfT8EWB3ANm69vb2NyWRbRQBeubi4KAGsHEjIQWXapU/wyXQ6jbdv35Y9KHxuvOXMMgQfOeDL\nxomZBtZN5tmancvXD8bJzOCSKxteKxc/z715wNydo74GGflzRyYyc9qAWeFYKRtkYBzpJ0IM2HQ5\nmjNHEcMEwsAWYEpJDLxs+KFD/tsZB4xmpq+NaVa+eQ4yyMjC68+cGQD4ICQ+G4l2HZGlPY/HGb6s\nqDGc0Arl6tKjruti0/cly9c0zU722/PM3+YHG+KXDwuvuT9WSPDrw8PDTnkDF45mxHB2WwYtNcMM\nPX0siPvsy4Y3/w349ef7AOY+eeUd8C6HCtf6zd9Wqvyf3+UoMsrx7u6uHDidlb8DJihpOz/Mhdc2\n2CC7X/AQ9HDEGoNrebNMYRh8L2sXz87OSgCJMdv5yMDZusFOMDShv17vgZOEkWLNKoYTZwk95Wg0\nnzF3zihCt4ihPMc7tnIUhHWPwZxlhbmczWbFaDuQlytEsi7zBd/xnlp5Zb7cF8sP/SRDSXuWj9pP\nvhw4Q/eaXywL7kvWJxlkZ2fbgQgCMa76MU/XwEftPe5jfi5iXDLddd1oN2PaRAadvYGGrN0EKPn9\nzDGAZ71eR9tuj2rwLpDoU4JzOJCUphO8ZRzWde7LbDYruyYy7y63M83dlucrzxl2iJ3g4Rv4FH1i\nWprH0Fv0FVl1GTz0c4YMIGl+pA07ifSZChrrLvSBg5dem2neNT32yZn5rcZLEcMmZPDyvuxlzurn\nKzuMDq6+efMmTk9PY7Va7QSbHGDlXf6ulsHksg2yLDGHzhaNxiJda93rrODBwUHBfy4RXSwWZQdZ\neAoHk0CscVDTNKVck+yeg70EWxzIdyCO35Rq8h1nXmILs6OD/SF442om692IKLI7mUzKzr38GNtA\nHzvktqFO+sC7jAEdwFyDHfOeBOBwfBVjO/+Grn4v88hGevDQer2OV69ejZbhuJ3saCJbVLvhoHdd\nF7PZrATF0I/2YbLNmEyGpUpUDuBgQle/ExpQxUD/vk/GfxBOZsQ4Cv3ywQis+759ismfF4L2u5Fu\nrmwUYIQaKPezTDrCYjAWMQBbQCyK2TtScj8MnxUZDB0xPpTW/bYStnIyY2RjB1M4c4rgZ/Bl59WA\nK9PawInxN00zcuBqTg7KvlYazHsNkkf8EUNki4gLYMACBbii1AeAaCchPKa+L//Dg1x2uGsGrYwx\ngTaXRvtZnGvKsRyR5Lmrq6t4+/btSLBrzhl/u8zo+Pi4rM2rXTlSWzPCNqa1OfR4sjGxMm7btgQS\n3N8McFFsnlvT2tFSDBSlJ4BaB3toC+fEzlJ2IvPfyKg3mGIsGEg7HFbCKHAiv4AR8xDlfwcHw/Eu\ntMkYoaHbsKGFzrwrR1ZtDAxcKSOy3orYjZqjV7gASZY57oN/ADOceUYU2BktG0/6RfvetMjlwzaa\nWRfndvgNmM6BMfQmY852Iet7+uy5qMlNlp/alXWp9afBG1dNxnKAJzuGACw+c1m2dUzNvm7bqDsA\n0KEmlx6vN7vzvPkHWmFHvest33tMea0Xn9kJ81qiiChHRBhMknVx8AKeht/Ifpq/sHXW38hDpiPt\ned6yw/Il3Wl+yqX18A6BIXQ+jo0xCQ4/bblKIwcZnp+fS7DT42cctOlAmB1KZ5Uy7jANa3xXs6vo\nL9O3j9ihGe+v2TnTM2OI09PT+NGPfjQKevD9l2yjZdx9tgxkYG95RcflMVsH8Fx2MHHisDlgi8Vi\nEdfX1yU444wjAVQvN3DAw5U/di6Zc9sCAqw5WeGjQWhzOp2ONupxUKVpmpJB9RzmecPxYsy0gSx6\nuUymXcT4bE2PxZtQYg/Bm+gZaAbdcBKRAWeT7aC7f8ypnfvn5+eS0Tw6Ooq7u7tSpcARb15eknE4\nugsd42qR6XRanE9wBH10wsy4jvG7dJ45y9gJjMM9xiX7rh+Uk8nvbGT9vZkoA1QIwSRnQ83vrHSY\nSCu/7FBkA4FiJy3vCHvEkEE0c8OYCD9lnzCNAZ2ZAsOBgmnbthgD18hH7G4M4PHwP8DLUWaPGQPv\nzIYVqmlqhvTnTdNEI1DjaAz0g0auxTe9UXwoIgTACs0A0QLNOBHCiIjz8/MRuDDIIRo64pUKj2an\nLoMef2dnsRaF9dwi8FaEEVEWiV9eXpbMt+eTvxkzCoNSsuVyuRdMZuPJvDtIgjIa0aXCU5k3uDBY\nETEqK9xHK9MYw+R3YNwxgg8PD3Fzc1OieQ7aIJN2GKEVDml2lDA2drRoIwdoALK04bISZByjTBYA\n+fVYMmCxsQVgWi/akbVzZyBN35Ez0wFDab7xGE1/7ssgkhJhAyuMLboOej4+Psbh4WGRQ5c6mxaO\n4HsnO/Om+5f1v/mc90NXVz3YucxRYtPfsu0gi4F1LUviv/OVZcXjz/o4P5fb8OdulzbgK2xQTQf4\nt+fdbX7Jjlou+ZusmnnPP/AwGRY26gIM5zl1lsK8wFpKA2pkkHsiYsRHgOvpdDqiIzTDPvMe5sgO\nFvrM1QTMtcEvcoNOtm7IZdzmwbZti3y5pJA1lA6k0UeP2ctRcnAt23vaNq7CUTk7Oyttw0MEMC1n\nWeft4zH/nXks87ZBrr/rN5sdvmybXXnJfbCuYl6++uqrODo6itvb26oty3Jd+7HTYx1iujLf0ItA\nCDTjvWAb61kH1eBP+I/vVqtVcTCxt06A8F4cE5wyKlmQj6z3KSW3nXh8fNwpYc6BI2jsZRXYMcbr\nje9s22wL+NuVf+hi/w0dLXsO4qGHvDbTwRk+t+Ns3c08WgYihuPKoIVL2R20QI6xs8jkcrkcbayD\n7jk5OSnHvOXgCbQi+zudTsv/zCMYBruHzjKOc4DaY0K2vdzI44CW6Dfm90vXD8rJzEbLoDNHrcz4\nZnY7EHwWMXYIssNkx87MkR0z32dDmo0hPwiLa8MtnIyBvr169WqkWFHaRDZcTtd140NrEWjas9Fr\nmia6ze7uXPTZzMTfCP8+ByCDYTuIeU79P3PDuBDyXPtP9MgbIfAe3mu+oN+OtFnJd11XNhaBTs42\nbDabkoHM4LU2jhr/Zj5FqZi+/bbR0X0Acx/6a/qxxubs7KwoEfdvND8xbFfPznBfAq7ZQYmon6mW\nx5hp4isHK3y2GkbDfLgP/GQQjMKFJyKirEGBZi7/sGNlQ+EIpcvRTFtk3DyQQU92sPyOiGHHW0dM\nLZsZ2BpQwJ/Zkcx86znwONChLpF1aZUzHx6bA3Q2ivwdMUTXXa7MGJlz6AdwouzVjmREjAALYwUE\nORPmn5rxszzBw9CEtcG15wxwrN/7fneTsGyD/FMDpVk+uKemI62vtu9pImI3W1gD57aZ+WrbdpTF\nHMY3Li+G77ivbXf3LzB9sq12IATZynYO/mBuvIbapYO2A86wIDs4UUT/Ad2uFqkFhSK2mU2ym6en\nfwGY1gAAIABJREFUp2XHVNMDWfEu0vTf6xE9p8YUAw3HmakcaHXZn9efEgDDThFUenx8LFkLlxs6\neGT778oF6EN79NVjp79dN6x/NYjFUUG+eM5BJebb/JEDQNBNkhGR9j+nLQf7fpU2zdOWATshXdfF\nmzdv4u3btyWL5znjvizXXqJRCypxf5YzA/Hn5+eyczLt2CEyr9tmwHOUycLnDw8PcX19XY7bQf7Q\nn8wXmWlOcIBfavR0H7KNI8vPWZXwrceBXDJe5tHZSGdKN5tNud9l3dgvfsMHeTMbaIEza9yDDvHY\ncK4tn3YCmTPmnCyps5qMaT6fl77ybsZV4ynvsn5/fx/z+byswWZtK869N9/J9i0iYrFYxPHxcZyc\nnEREjKo3IqLsbWAdQD+9V4mrHixDpqvluu+HcmMHDvZdPwgn08bKgpoFIGK8hXAG9BZMC4eVlJkT\nEGplWQO4TumTeaO0MWJ8YKpLcGiLcTmqaKDL5HmzHxjVETPWnEQMjgn9Q0gKuGma4sz0/bYMNEdX\nuLJitTHMUTqP2W3YwPjifwM5jCV0x0l2FAgQixGlrbLGthmynRHjSC7tOrqGQFEPbxBqBZ/7XeNV\n09BAKoMLtzkyPIle9J11DPn9OMhkwhy4yP2Gz5w5oz4/X1nWbEx8GLbHawCRHaDcLtHQg4ODuLu7\nG7VvkJOdBNM00565Q144HiEiiuIkOotMQo8MFD1POITIHMaJfmSHI8+5ddJ6vd7ZjRJexHCwJtmO\nIO/FIct6EFnnfXYqHF2kLwbEzrbkEiAHQsy3DtaY7syrI84GXzzP34Ael/BGRHHA/ax1n4/bcNS7\ndlmOATERUSLv8AVRW8sq8++53LY1Ll30uqNfxcnc5/TZ1kCnrHtf1PdIf2bZqX2WgY35wHJk4JCd\nb7dpnsjf1wA5dDYfuXIBfc6uxazncjYsO6h935fgkc/uAyjnShfTlt+uGIqIsikVO3F7N+C+3+7y\nzTNuyw6rnUDTKQets542/VlLzrIONt9gPOgjbAM6w+fTOShDP5xlQ1a9QVfEGESa3tCWs5tns9nI\nRj4/P5c1rwan9Lmmt/PfvValvHxTvc+BN39Xs785gJPvR15OT0/jxz/+cZln2zR0TbaNxmFZzj2n\n/i5/jiwy11l/Z5zrKhLwHzZiMtluynN1dVUwDRgHxw9bwlyu1+uCtbhyJZgdEngB58c7LYMz8vwb\nO9g59hw5SApdcZZWq1X5nPZYN8q43V8uAjE505uXwGDXvBlSxlrenMkBFAerHSxt27bossfHxzg9\nPR3tOVLjW/p+f38f19fXJaNJ3wl+Wb9kXgf/WB8yLnQo/XBg134JmXMHeCOiJLCsY2wnzIe/NZlM\njE8Gf1mR2LjZAbWXbWeNNvyMDYcv2uBv+oVAsbuUNyWwg+l6boyA+2fFY9ALeMxnJRG1BITd3t6W\nrdZZoAtAxVAWZdg0RYublvvAMt/nxc15LaiVUg18OArt7wF3Dw8PxaiiPC2gCCbvduQIEAZNEHgv\nAmedHpEq+sL3ABwARnaCTQsbH18Z4DHu/F3pO4gxXX4fvGRATL/cb6Jcbt/0ph8ou7OzswIW8lUD\nybTDZi15fi137p9ljHYAR9DHAQB4kM/2OQ/mxYjxujuvt/IOmgagPtbGdHaE2DIKb6HEcxbFlQQY\nI4CJHTZkGpmhhNlgg/sYj88LzUEB6zNnHM1/pmvOathoEb3nXc6ce05xMqH5yclJkWM7UuaHXPJJ\nUAl5NniKiOJ8wtNuk376SJ3Mk5l3PE8uITLYJjoNaOA9tYv5JbhjpxNdV3M0rQdye5YlZ5LK59EE\n4TTTMduzWmAn66za/JpWloEayPe9ln2PgX4hM9ZpBjeU+y+XyxKkddDYWUPTOgNHPs/PEV1/eHgY\n8VEuTUVfWE97N070LzoUmef+TAv3gf7beeN+AznosFgsRuckUgaHPBvEopMp7aOyBUfJ82l9R2UK\nQN56JFfNWNcig23bxuvXr0t7PMMcZJsw4vOm2V2G0jQvJvH7N+CCXtl5td2wzd7Xlm3dj3/843j1\n6lUsl8uR/PE+2vdzdiD9t8tg8zOmA8CfZQDcw9iMCR3U4D5nB2l7Pp/HfD4vdHD2Enl4enoq+xXg\nsBGA9XuQT+5BH5vW9NMOm+fG9zjz2LZtkSMu+Ojo6CjOzs7KsWBky9Ejd3d3pc99P2wYad0AZkVP\nuOoQPud/bECuenCb3pDIdgFZ9KkQ5hV0D3RlHxDjV3jUQcXb29u4uroaZWGhGQEE41vLzf39fQkk\nsEkRPgJBMx+ZyDxROm3dZFphq62/0UeWyexD1a4fjJO5Lwq1796IcfYyOwfOZloJGojVIvjuA8AE\n4AkTcT9MWIsU5CwlyskGFMDDhfEBKLvdphkyNzAQ/W2a7Q6Ox8fHcXZ2Vg629TsNHA3YuTzuHMVj\nTFnhO9JhGmZnHaUE0zsCBMjzZgVEp3kWsI6QQSuUFPd5x10cNjvNXF233SyG7x1V/D4Qa5p7jLUr\n82Xms2zg4CcvVDfwxxk/Ozur8i7tonBevXoVZ2dncXV1NRqXLwNEzxVHYHhTJjuSfEYQIhveyWQy\nKqkhImej63nJTn6mN7+tDFlLc3JyMsqOW+E6Y8dz8EeOVAI+DRShCVeOxNqBoV342n8zH8i8HQTe\nbV1moEL7bdsWh83PZxm1UXDQi0ytgQj9Z848dn48ZuT/+Pi4ONmeyxzZhM4uobThBsgCgpFxG04+\nr405y6JBXk3vGIwA0K1vacN8aNrkoEoGlgaitQivn806dbgpIvpdnWG+yPYyB2vcdpYp656a01i7\n9n2f7aszd5Yd9NdisRhtNJKDZDznAIf1IzbSNgFaWx95YzAf0WMgPJlM4vb2tpSXeRdWggq8l+/h\nAyL99CnbO9pHL5jWlMmtVqtYrVYjJ4Bgu0Eg9hL9xmZa2HYqJxzogp7ob3iYYHTEUF5sZ9N2F7oT\nGL68vBzpKMu+cdU+/qnZ0drfumOHN6Bxdgr3vQPeQz4+fPgQ79+/L8eHWD72yTi0814FxqD5uRx8\n4zvKwz1e/7aNykFrrzueTLZn3s7n8zJ/eRfUrusKb7vaazKZxGw2i+vr61Gg00kK8zJ/8w5ky3Q2\nr4BbyKgxJmMx0wt+ykFkJyW6rovz8/Oyz8Tr168LnU9OTuLu7i6Wy+WoustyA4acTCajCkRj+MyL\njIlKB45ec3LIPGed+vz8XM7qnk6nIztg+bDenM/ncXJyEmdnZ0WOj4+PCx62o+8LjMFcPT09xfX1\ndbx69aq829jOgS7jIo8DWriSw/bE/f++LGbED8jJjBgriRrotPH359mjZqJrQhMxLGTP7fgdGXxi\nkFCwBi55swuUkIGNIycGTP68bYfsCX3OEQZ+eBcMyNoWG1gMnOnkEkb6kJ32LHB5DtyWM5t+D2Mz\nqIeuVsymD/PBPYARR8EM+OiXlQTAF4VmITQ/PT4+ljOKrLwNEjxmC6N/13iReey6LuruZ/1iPin9\nsDGnj2x57d06eS/34mg+PT2VXUtZO+AxWXE5GGFe9kYcPEf73HdyclJkDSeN+fLaOkAj0fHa5Xk1\n7zOnfEaGkveR2QdUYkyYpyzzeZ4IeuRggwEstIkYNuQ5OTmJ8/PzkRxHDMbSfIcMZEcTcJIPRrcs\ne64McrIj4fm0YTCvODMI39uh9pw702Gdx4510I75hXbwibNIvNvyyt+ULwKADYThAYOTfbxj+qB7\ncCLpE/NgPuH5zWYMZqNp4mAyrBHyvFkX2vEj0LXPYfOz1nf583xvHicX783Ojnni+/qy7/vchvUR\nY7XDaxsFLx4cHMRqtSoHxGfbaxuAA+flH3lNcUSM5ILgkHkcx9Fl8Og3MoK5rBeasqFQ3/cFfFOK\nxlgdYB7mqom+Hzv11jNkkKiw8LFLOTNgPQIfk5VgHtj0zEc7+CiEGiCEjg5cWTagoe1z27ZlX4DL\ny8si8854jgDqy/C7rovJwRhi1hwYfRusQ97eO8Zj5kn0aA3s54txzGaz+OlPf1o2mnIQ2HPFM/Cu\nlzNYN7svGdfQP4KrERE3NzcFr9VoAl1zZs1BMdonWGMHE951MMeZJ+MJ+Ij2oRF4CT0ZESM+YfwE\nYbxpED9gD8pmXfUBne1gRQyl5/w9nU5LEIDzwuH/09PTMqbVahW3t7c7QTMnMxxgzwFn+p/9Dcsj\n97vkHnvo++mDEyg4yK6IML86OPP58+c4OzsrG271fR/n5+exWCzKe2p8blmkHwRR3r17V/Sd55a5\ntuyb95kv63fjUP7fF0z19YNyMiOS8Y3YOULCv01sA1+IYQNl5qs5qlmZWThcX933fdmkBmGC2Dm9\nzjtslHn+4eFhtAAcQImSIFvpCaSklkiRI8Ew+GKxiIjtznBWiBmUevc6O1YGD1YKmSZljkQzO9YR\nUWVk7qP0w7TkQhihCcaZSBolBaT0eQYFDp292Yj5yoaTc4Wycc98kfmtdlkBFHCeeAG6wN+ZDwE/\nefc13mvnOG8URNvM19PTdiv/i4uLHSeTy4YVBYJBRVl6XBm8+oB0QBgZKIwiShl5rNE409tzRh8x\ntLwXEOCt/Vl7Ylp7rVV2zB3lhmd8QU/PK3KQAQiOF236EOmI8foT+A7n3IEmLspIzX+5f54bAwmX\nk8MjBwcHcXFxEZeXl7FYLEolgvUHOsHOptdSWraZ0ywfLlc0Xb1boQ00sk1GBifYQTc7cQY85hMb\nQtsD+u5sLOWQ2Wg3qbIdzkQuAGQZMNV+15xh9zHbox2ZePm4j7Gdyn97DriY91q7nq/tvf4+ymeA\n/FzdURuv+c+Ay1kXr8umTZyuiGGndACcQQ98yb0Et+BZ9OZqtSoHu7vkz1k+Bwu8Titi0IEErc7P\nz8sB5C7rhsbjed6dg2x74fvNZjhuJdtHgztoZd2LXccuoveaZhtsI/vC0Wl2fGp0dXYuZzfoK+84\nPj4ujiZjcla1bdvo+i4KeIORCj3asCM55s2I2NnTPd8ztg/es6He5nDE009+8pOIiJFTkgPKOcCH\nfkfHe14cXDHYLva9H8pQSQI4UGmsmXWr6eo1iW3bFj4HR9p5IKtnXGUdAy9RZWQe9LvpH/d7sz34\nGl2IfTVuxH54p2PPUc7+Gsv6nRFbLHt0dBRPT09l+Q2BGesD9w99YP2S38UcobfynPA/9MaGeDMi\nB4+yDCNvPqKkbdvYCDdn/PqLX/wiLi8v4+LiogQOptPpaF8NB3es9zPfsSkU+sBB7GzPmQ/TJ8sD\n8+vvkMEvXT8oJ9PCjo7KwDYrEkeRIB5gwN+ZqBkUMdn7wBzPwrBMgpnXZTN2aCxIzujVHDCUYXY6\nqNXGkDjb4WiqlRCH26IoDaZyNBa6ZdAGLZ3d4V6Yi36P5q7vR0rFwBB6NU0z2qCFdp3RjIiiXGqO\nFAvR7chk2llATCfmwOV4KHYD7miaHT40fXwxJw4+mA92LvG1eRsgkCPSjINS37x4HQXBO1H+RNJ2\nXz+UjvnH7RpM5rHyg/IykMOwef0OYyKKzJh2Qe94nZPfxfzj7OXshdcnOerI3468RowDGmSQ4TkM\nOBFZFD1jxQBhTF+9ejUqmY0Yys+gK+9wOUtNH8FL0JP/4eus2C3XlkvLlddumx8dOQeAe/t53gm9\nIqJ8b2eO8Zq/+I1jR5QaHsGQIouPj49xdnY20hkG2Z6vzJfZYWTcni/aIxDBGHIwxxfvPzg4KGu5\nsixkx2ufk2meMzCjv9anZV1m7DkiSu9yQCjrqawXd23pbjVPxDiA6OcyyM7gxIGO5XJZjlGKGGem\nsT8ARINa+uEya2wZvAiARi55nvYzDeg3sjCZTEa7STMGA1acQc+Nx29HY5eGw3yzOyw8T5bJwM22\n1cFly4KDgC4R5Pnn5+dYLpdlWcXr169H60+NSewQ0F+ciZqtJENyenpaskk8ZzoYR+R7XkYUkGfL\nF8P/tcv9gB70yXqyamNf7v+d3/mdODo6ip///OejkkfPp+UJXnMJNZ/njWwyDzB3OEV935cy2dx/\n3gk/WM9iL+F55osdkuEnZzd9ZmLOYrbtUP4JD1pHW0/ST/AVfV6v16X8nDWHyJ1tFPYzB3DMi3Z4\nDw8PS3CI/kAfxgTOc+WRZXHgp8Ge4ETbOSI5Qb+NI7B5Xv6CM4+t8zwSKGZHaGyK+XOz2ZTAxvn5\n+U7FD/Q6PDyMh4eH+PnPfx6z2awEUEiC2J5ZRi0j9Ivf6IN3796NlhhkmYKG1oPwJgE5Y0LrqX12\njusH42RmwxgiQs2xrCkUKwEzEEbYYIXL/9MuxDXIdkSIv6l9d8ScySD65bN5DFopyYH5nMkwEyKU\ntEEG0wbfIM+gkmwPnyFAvMdKrQaKDNZsNOxkO+PFvRFD9sPlPigbhMdlQl67BzAHhJjxvSAbY5HL\nMuwYeBx5DCgtSjtMi5IVj4hG0Ufay/znz3I2OyKqWcsarekftCWzbR7fbDZlEyAUpp053gPPnZyc\nxJs3b3bemYEMfeAMOa/HpO3sEAH+IrYRYkpnI4aNiQwinSXNmRYDGssifc30dRs+h876gbIgZIE5\np828lglgQASWrBwRVDutbTtsEoCxZH0xgQbGTOALB/P09HTk1Hn87lMuu6xVShjsWD8YXNixyRUJ\n8Dt9ccAMQG4DjEGcTCajTYRoC2BiQ2Td44w3fAbf5nlkzPC5ASw8ZWBiRx0ZcEABvvS9zB/LDfbZ\nnIgYzXeWAzua+xzW/LmB2Zcutk+xTXQGybSxPvZ79/2/D5xnXZWfyePl3czf/f396HgheMftsOW/\nnT0cMmwGc0I2EdtBH7uuK7uvwsPwBPYTPiATeX9/X3QV8kMfkEfmBXvisWcHY0Qr8R/3U1rvtePM\nYQ3w+VkHzDeb7YYiAP08R8YybC5E4IYyZN7pwEQOVuYKp4ihFO/29jbOz89LcDZi7GRmHt/9f8xT\n2STm+7Oza8Br0Ovx+9mvvvoqDg4O4mc/+1nBGDmp4Pdir+AlfhuLEfhGt3v9MNjQ5yjnoLNtKrJj\nXWa+AbsR/FutVqONH9ncZ7VajXARODBiK2fwNQkI5NF6A9tvJ5UzqAligD9OT09Hm8zYCWKM2Qay\ntpJxGY9xZWxAHwjM2hEHm7MZp5Miz8/Pxcl0UBZcBKY2ZsaHAA9ad/lEh6wDzs7OysZO2DV4iQss\nMZvNCp9EjNfgsv7z6uoqvvrqq1ISfXp6Wg2ENknXcFkvs4M3G/eZ5/k7O60OprvayrL3W+dkWgCL\nko4Y7UyWFYPJamJ4AXAGLOXZSgSAy4bDjosdIYydAVmOaCNkjii5BhxgSpSLaL93QYyIURtsdewy\nMjuotYhlHmcGCDmSDg3yM3zutDzRm1pG1bRGYbpdQD6A4elpu3070SPeRW0+4I4xE61CgHDKHCFi\nXDau/sEAAHzpu8Fp4Y9mnNW0cBloeaF2+T5iVCaRn8u8Cc+Zp30Ph5hPp9PiGEBT95nS61oms8bv\nbdvGbDYrvJ15ivdnJwvAYyMOf8LbBvXeRt3zZB7MIM2AwgbMOz6bnzEKtIOj5LM0MTjMP4aUfk8m\nkxIcMg/xHjbbgh85m4rAhUutaDNnFB1YyYAvR0Uz77g6gPasl0xb6I+jC/8TneZzorLOmMKL3oQH\n2XG/HGWGflz0B57cbDYlEuwKEXbIszzkTEUtc5F1r2U8j8WBDmjX9/1OSZv5koDW8fFx2bHZdK05\nnLl/+f++H3bHtp4JAbCIiKbdBd2WEwNF3+PvDCCYiyaVJ/aVDGbuc22czkxC4+VyOeIFz7M3bMu6\nFFlDvs/Pz6Pv+/j8+XMsFotRNQJOGBlHnAGy4V7bayBvW4q+h3fAEtPptPA7+sLBkkyPQiPNtQHc\n4eFhGYvXqpu2du5yMJfsCms6PaaIYc2YwR9BGxzl2WxWSiXdZ2efkS3oYHuP3l2v1yVQBk85ALSP\n5+s8NeavGs+Z1lmerY9y4PLg4CC+/vrr+Ku/+quyp4blBFqhOxmHEwXGXDjtjJnnM9biwpYY3Gcc\nlvttO2k9Co/jyNjpxBnxBopN0xTnknHjhDoY03Vd2anY57WSXXUFAbSyDCGrTsJ4/DiITTOsY8Tm\ngWVevXpVfd64BJq42s22nc94J5ldeDZiwNLYfXAl1+HhYczn87i+vi6bDsEP2ExolKt8Dg4OYjab\nlTWyyKDtNOtlz8/PC015B/N3fHwc19fX8dVXX5U9DKguc/WHec32wHaQdz88PMTFxcXIltl2+Sq2\nQbwN3cEjfvdvTbnsiFjpcxMwQkonthkiFMEoAyUnj2e2n5VWgvySmRXhc7QCw+h7vIGBJxdGYL2l\n1z5i8Gw4iL4aHOB0ZWNmxnB2EubMmaZahMEGCMNpZyDfOwIz6Tv3KTOehd4ZyZwl4X82UHn16lVx\noGgf0MsagpxdQXE9PT0V4eXZHG125J8+2qGy4O0IcN9HI+fZPMo7XAINzTLo7bcPjejo9hBkxulN\nLbiPaKKzuFYOztoSuMiX55z+s1EQ9LeiscLHwSTrAKC5vb0dgbwMdu0Emr8y/xgQ29E1neEnlxrZ\nqW3btpS52MjZGDuK6aCOL4yqNzDCkFGqh6Pt0mHLPdFRsidtO5RDo/S9oZOjiIzNWX3Pg+ccIJCB\noYEPOo5NE5BRsuZd15UDnk1LHDECCuhbLxvwkgCeMViLiHJwOG1nwIu8Z91nngUw2cDZ6fL4DeZo\n22VoyArzwVrNDGIAXGdnZ3FzczP63jJsI58v8zpz4TKkYmde9E1um7GZFtDHZc9cX3IWh0OHx/eW\nPlb64H5kYI1O7bquBAu5cvUPvOtjgDyu09PTYgd9pqazKgZczqqdnp7GxcXFyDagK8g6sBkGG3Lc\n3d2VgF3btnFxcVGWJOQMgsGVbXTmhRzgBag+PT3F69evo23buL6+Hh2J5ioD9NfHjx9HR1Ww66SB\nvisAeK9pw/8cmcLxVnnfAjv89AGga51M4A3gzeddt7u3wVgO+oLBckC8fr+eFC/D78YzNV5HVxPw\ncIYuYnz0iCsu0L2UhM7n81iv16VN22HGYj1lOTUm9RjNlxlHof+dbLi/v4/b29uSmXt+fi5rkHMV\nD/oNGWD+6BO/yZDRdkTsYFs7wPQzVyRhV+mr/+66rlQhoAOcmMkBUet404ugLeM37Rz8AeuQxczO\nFnJGW9gD6E/ZLv1+enqKbrOJg8kkzs7OikPMHEAT7MNsNouIwY4Z79rR974ajBfnF9v87t270h+w\nHu1k3VPTP/CZK7lc4ejgP/wBrRw8cSDGPP5blcmM2AWYte/4G2I4ug7xrSRRlMM6JPKjzpOOgcEw\necPaFMDX4eFhAbUR4/U4zqy5bKFphnIgjAwRSTs7RJ9yZjI7tRFR2ofheT9M7E1HaAPFY3CSyxZq\nirD8HwNj+ZnRPcngokTcHplJyj7W63UsFosSLfQuuQAOlAqA3+vKuL9t27JL5enpaTE8OWpjAwGP\nmG+gl6Ps2RHMY3abNZqNFEAI3zXjkorM+8wlGV8bCtYczGazsilUDeDkcpF8ed45D82bKNEec4/C\nh/cA77yfzCAAxEDdPG1+Nt12wK5o7HmBh3wWJvcAQJx5w9ijDyJiZBT5wXiwSU3u22KxKLyIs4mR\nsj6wk4PSzrvyMfc47I6KsjkQmQvmKAcHHO32UgH6zt/Wm47URwxbzE8mk7i4uIiLi4u4vb2N29vb\nkTx4V0PAJ/NjveX5c+aJrABABV72rpkY+tlsNgqc5ABQLiXOOinzDrRA9wIi0SmeewMc8+/z8/Yg\n+levXpUdTLNuyTLoK+tMnJpRabp0gNvI4Js2cPRNqyw71X78intfZzvAZwYY0K/v+yKT9N887Wyc\neYg+u1rg8fGx7MjpLKn7w33wVtM0BVyen5/H5eVlAXLYb/QpMomeog+cJ+myOcue6Wn5M61rTtO2\nz00JwjH319fXIyePsjY252J9FrshN80QLPPmd+hX5gYZs20iy0Jb0+m0rNOjj+Zh5pT15vAuwT2W\nEQy6b8S+o+8y/+z83SivXmsoxrYqYli+kB1+X2Qwud94xPbIG/1Q/dP3fXz8+LE4SeAs28ZswzPt\nauX1HkfWNciVS3Aps3YGjbW3Du6A8eARn5HtYBpZ0BwMstOR8ab7C6a9vb0tWCtnBglQgM2pliG7\n6uyk6cB7cnASp9C6J5c2EzSidPXz58+lbdshyzTfTSaTmM/nJYCOcwdme3x8jE+fPo1khnbgcfMB\nR83lsnY+u7m5idlsNgrUYGPW63V8/vw5Pnz4MAqGgrnsXNfwUtb91n/MpeeaC3kwdmGueJfXxecA\nce36x3Yym6b5g4j4R/rodyPiP4qI1xHxb0fEx5fP/8O+7/+nX6G9qjGr3cfvg4ODssOcSwQyMBwD\n/8FxpB2DNBTVluH7UrrJfWQ//HdElMge7RjM8f1qtSrM6WgjTOhsE8zGezB+PEemwdEkgC8AFeDo\nTApGPxtLRydgMn4DyHw5w2Qjl51OPnN6nz7TXxzLzWZTFrUbwHAf7/W6P0oUDGjhJTJDPsLAWaKI\nof5/xzkUP2ZeGYOGMQ9ZYbstG2+ezQazdi8KBcFGUfIulLzLSGjH81Lrl8fgKBxzkTNl/ACS+r6P\n29vbYujcXi5BtnNsEM749jmWdhwwnrQHmIWXnKXO65hyphNjjLHNUUbG7yCQaWVeWSwWZRMfgjk2\nxJSHHhwcxM3NTeFNly3RvunlUhXeC134bacbubLDwfx7owcM/YcPH+Li4mKUAXv9+nXZ2Q7nmRJR\nAL95x6VYdqgjxhvEMA8ufec8X3QUYKRpmgKG2bqecbjdGk9lIOR5qxl6AnUOiLj82/QmykyJ9Hw+\nHwHvPDf7nDv3CyPtcew61lvwznO2UQ4i+B2eI3+2z772faXCYvvHyAGys+XsDX1er9clA2laYK8c\nQDTtCL5gb+fzedkB2Dog23iDfUq6fdZt27blXD0cVfMqtg17gkyRBULf+V3mIfNdnu9sU+C3ful5\nAAAgAElEQVRbqlM2m005/mi5XJb2j4+PR444z7BBCrvGIs/GHZYldN7j42NZU2on5ulpe6beer2O\ny8vLUv6a+ZlxAnC5kGXbczth2XkabOpAoxGv+v9kL/Nl3mJeve7Qc4B+d5ACejkoaOx0cXERT09P\n8fnz52Ib/c6a01zLptp21Mbi4Ca0tNPrMlmwzWazKUffWPfCx85cRgyYhLXyOIfIojPntiW8m78d\nDLJOcEaZ8bI8J+sqnnPQ2ruyW66ZEy8l8VzwTgIM8Di88PXXX0fTNKX01Rt40Tcu7CaVSNCK73gO\nDH13dxer1SqOj4+Lk819rtSg/1Tgec4jtoEC9AB8ws/V1VXZLRp7RNDZy5IsJ8aoxlkOQpgPjXMj\nxgkWLpfNm0/g1d9YJrPv+/8nIv7OS0cnEfFNRPxZRPwbEfGf933/n/5N2stAjisTxNEPIuIud6Qt\nZxo9qep/+dvKH0a2gFqw6AdZNkoWvG4LoeDevIMqEVycK+5l90X6QSQoA9Gnp6eyQDsiSr02f89m\nszg7OytOpgUSYwzAzZmXzDg5i2cQ5+8p0+M7M382zihAR8f8LJliKxvWSpHutzLE6LoMMe/k6T5b\n0dGO+z/ikxgDtZrhs2LIxsR0q0WZ9gFRt+uafJxM+JvSYmczswwZiOTPHRhgJ0IOeWZ++Z65mE6n\n0bZtfPz4sYAj2oPHvKjeoBD64DTnfpmOXJZ55pC1KLyHNk1XIpSOsPKZd6HlMxvCiBgZT4Prk5OT\nEcCnPQyQFTJ0xJBzDABlSnZKacOyDr3Mq+aTmtNgHkTW+aE/6BXKEjGgl5eXsdlsSkDMm4zkNa7Q\nzjTLzlJ2Qhx0ow8YPgA2su/1kR5/TaagUQ4C+Z5cFshnZDoACwSh4C0HocjwT6fTUtrv60tOh+9B\nZmwr6HNuK7cLf1D2ZIC3TwfVeCV/hlOZecn318Zg2tieMefsjoh9s32IiOLcPD4+lnVU3gQOMIw8\n0W9XaLBh18nJyWhTrfv7+1gulyUQSZkboJn+eo00MoKc28ZYF9TmJvNnDrQ4SI2umM1mo0olghwP\nDw+lMgG9D2idzWYFQ7BGz/KGfceeWxfzg+5l3VrXdXFxcVEClswjc9a242Oq0Cl27NBz+4IZZrEs\nx20zrgCp2bHMh+ABHOnsZELzHGg0LnHWEAfz/v4+Pn/+XGieHUt0km1jlnfkIgfgsv6kjw46eXkV\ny65oc7lcluVYzDMZdwdMmQdnLn28Cf11qW12IKxb4BfoRLsOYBhH7dswhgC06WobkenoDOE4WDE4\nftAGeni5R0SUcnMHbox3vQstlVHO0ll2TC8quLCVTgo0TVOO5fJGgh6nN867vLwc0ZFS+d/7vd8b\nYTiCUPld1qm0bxvC3iUEho13s42GNxk72CnbFnTMl65fV7ns342I/7fv+7/aZ1i/78qGJ2K3tMjZ\nOHvnZjwTy4INUa2ozGQG9CjUiCjGyOV49KVpmuJgGmA7YspnTAYTkssxuZhQnCWcyaZpRls8O/vg\n8lUUDk5Jjq6ZdtmI2FCZvo5OZoPKeMyEzCcKxtFPK1rT0oYMAcvPeIMHnouIkvkEjGAwlstlKdsk\n04Qz4vlAoeesQtu20Xfjbc49p76Y91/FMGaQkq8MnnFk4AtHrruui9VqVdbXeEMdFIWfyX3j5+Dg\nIC4vL4tBM0+6v5R5ffr0qZy9afnjssLPPGgDnxV3bo8f5or7cDC9KyVgMGLY9IdxeMMBjIiz3g5U\nMQ6X/EUMGQjKaMlQoiPoHzoKYwGApn+AcdaCoLPsTJsPTIPsaGZ9xv+Us6GHkOujo6PRTnzO+B4f\nH5e1OavVKlarVUwmk1LSvlqtRn2FPtZjBmPmY/MDtHOJLgEi5BAaeb1nAaP6m7az08P7uc8Zc9sF\neI9dvtktMWfM7MCR/To/Px+t9eLKgbl8eT5NfwcURsZc8oe+wvFn460sp35Xfvc+xzH/z322j+5H\nlve80zC2yHoImwHNKD1eLBZxdXVV9ByZTmSLvz2/zCfO5cnJyc5uspPJpByhgvNZW87i0nHPBSAu\n2z87M7X5zno/85CzVU2zzfQS3KPEEYA6mUzK2lAHOpAdqiIIqDEu7KPnzqWJ5reu68qh7ziwzk7y\nDDoE+cG5z7Lpa2QfmvFWU+ZZ67DsrOfL2AMstC/AiuzbOfD7yQ6fnZ3FxcVFrNfr+O677yJiKON3\n4D3Pbc3+Zf2Uae4+dMIYdpzAXQ7uUSILL1CB4Ywm//d9XyqOrq+vd45eeulo9NJdTdOMqjqgs7OF\nOJfYy2yjnPxxhYqztNg+J0icAYTm0B968D/9ga7gMq8xfn5+jtPT03j9+nWx9WR/7TDDwwQs4Cne\nA3/Zrjig33XdaBdnsrgEsLD9LBVh/mzLF4tFTCaTePv2beGpzWYTNzc3pSydcTq7a9xUs7/mSwdZ\ncKizbufdeV5dlp/v/z6f79flZP7LEfHf6v9/t2mafy0i/jwi/r2+76+/r4FRRyuKyMwdMV6cbDBp\nR8nfkcnI78vAxBdAionhfTAYEXBKFDLhURAwrLM3RF+IkNrhcjtkGbyxgZWUI7Mup7NC9N82qnxm\nkAQDIaQGkBlY2BHxe21sDNYsELTLZ8604Ow588rREAadjo4ZaOboFCVYRM/on9cH0J9crpLXK+wb\nN8bfYOrly9EOyeZLvo/vAX207bWZKFUilPf392WbejsWnvd8OfJ9enoaR0dHBTQ7AAFPuGxysVhU\n+2sAz4ZDdgoyiHD72TngB0BlXkEuGadLOgBcRHZxool4YzSYJ+bMax/gE9pi3pFHxuOdaT126Edm\n7unpabS5AKWA8D3l7bwXPuRCFjLAMT9BC/ghl7Fyv8uNIrY6ZL1ex3K5jKZpSqR8tVqVZ8ms5L54\nLrOjaZoQILHOhlbsPohORNc9PW13sXz37t1IFjI/eSzwNHQy0POaHL4z77EJEhvA2GFyWzwDoMDZ\nzgC3ZnzdV2TDevHlrvCj1h/Igfk/97HmZJpna1d+xvTNuiPbl4gY2Unr4YgoAM8AMmII4H777bfl\n3D/rcewvfIKMenkJzgHYAICHbUaO2LQFmUYukG3soQPZWZ6t13NG0+P6PifE9KE/zCtn/TJugmEE\nE6yfmqYp5X0E4fw5OtAZmVydYeeAQC2OJhUr5gnGg651OXONz7KeMi9nfqPtL/Go27X+9rzmizah\nE5gEXQjfXFxcxN3dXXz77bcRMTiY7l/E7pp48+w+wG2ed5+xDbRFn7x2nzGwaRP2zM8jH/ywYdFy\nuYybm5siCyQOyrgiRiuzXRXEPLOJH0FdBzJwWJwBhR7W1w6QshYeO0cwJAeGcVgPD7c7Pd/d3RX+\nztiQwB/jxBEjCHN8fDw6G/ns7KzYczY5PDk5KRU8BO/z+kV0tStu6CvOZtd15ZgSn4eLfmS5lyvy\nNptN2ZeETcH6vi/7lTAGKiGMJRwMyPJq3QM2wl8w3sp+EPPA3OIkQ3OeQ1d+6fpbO5lN0xxFxL8Q\nEf/By0f/RUT8g9jy7j+IiP8sIv7NynN/EhF/EhFlgXVRINsbykBQ/IAQmDwDHCugrKiclYsYKwoT\nlgvHw0DWwJJMikuqaMORFSLyEWOh4b04me4HESCiF/f396PSg5wZ5CdvJGTFB30cNcsGhu9yhMqA\nAho565fpXlPIWfm7tAPQjQA5uo9S5jfGzUbJ9KBfjBEQ53WZLpt0lqoGQHkGxeXSgGxAET73CUpk\nGhU6/QqgD/rhtDm6Rv85JBsgkp2ol36/a5rmzyMiPnz4MJqj169fx2az2VnnQT8oQ7u9vY1Pnz7t\nRPZrsucyccZgpcj81IyS2zYPA6CgNWP0BiouD8UwEsCwnFpZIgN2LPLmWfQTecwKHYNHiTa0JLvJ\nIebI/PPzc8zn82IQMZAED7L8uZzfPJppC13cFnLgCC1toZegx9HRUdlpEIDRdV0BBrzbGT8+c0aI\ny7JjnrLccu4rmWgCPF7fB2/XnKhaIM3y6Sxm/kFnAtien5/LeW58Z/2P7phMJiWbmSsF9P4ic199\n9dWoTznTtP0pLey0B7DyTqKu5KllHPltO/nFq0LbrItyAMRAOcsODqbfC0/e3d3Fx48fR7sNW04d\nOOWiMsXBKVe+OJiMjOOAkpEnW+pgBrq164YzN3m3nYiIsc4puqxpw7v0ms8ZufnIDgljbZpmNA7G\nBj1woBmjA2d2wjebTRwfH8d0Oi34gXdYPvY5xs/PzyWI6DP9HDhGRzNftovWlTX+ifR55tPxrbt7\nIxg458D1ixwWmfvRj360Y1/gQTDl+fl5zGazmM/n8d133xXZtA3Kjqav3Dees87IQD7rHssTZdrW\nCwT9CCpwvqw3PYQvcKiur6+LU0o/cC5qAfOmaYp8eddT5Ms6x5VVjDWPN591y/IIaDSdTgtPN802\ngwovw/OmGbjN+Lbv+9IuspMDnZYz6MSmbXwOT6ATCOpQqWg8hDyA77FNBL/B/BcXF+Xs7JxpJpCA\n7oG2V1dXJaMJrW5vb+Pi4qLYX3SPl+BleTbv+jLGhtbQEr1uvYdepy95sz8SAF+6fh2ZzH8+Iv7P\nvu+/jYjg90sn/quI+B9rD/V9/6cR8acREW/evOktbFYipJhR6I5CWpFZcWaA3A/vHEXhs7Bzmdg2\nGERE2ZrYCitvvAKDE7Vp2+3W6TAmytlM4PEAYrwbo53kiKF8D2XrEgIr6wzqcobEmcOIwVmC5o6E\nmq415Wun0+/mc4AvzOnIGvd54TpOPEDdxsIZBEdxrdjc7+fn7U6glFVlQFwDnvwNaM+ReguzN4sw\nHbIj4N/7rkJBGX+cYhSE6f/4+Bh3d3dxfn4+3qkyRkbwU9/3fxwR8Qd/8Ac9fTo/P4/pdFqMmOcc\ng3B6ehqbzaZshOCx+x2meXbq+J0Vtp1lG2nPLd/jOPI8ASi+4z0YQgeB4COe9dEw8B3vh089DkfK\nPX9sHGPehEa8i4gqjiZtEDl1iVLOMnDZqfVYM6+aX5FvLsbkLBpZYOSJTBHGm+dcgl+bS4yldbP1\nrfnGDhH9YGfoiEG3Md/ZUcr0Mc9l4Jx1vUGhn43Y6t3lclmyG+aRzF8R2wDp+fn5aC0ibb60W2Tu\nj/7oj8oNyLFlZ3g2Ru1wP04XoAe6bTabUeCSK8tjRN3JbJpmZ59Z85bvQ2+b7nYM27YtWRjLuEFR\nRMTHjx/j+vp6tH7MgUtojcyiq8n2d922xBk9SADCZbXO3nhODECxqQBmaG362W7xfTV630c5Z3QU\nQIt8RvKYdxkbDiKyw4YfyATOBXLqzUQyAKf/DmCRharR2bgJu0/w5P3792X89NsOQF6X6aBg9er7\nvY7mC6dF0+x3MD1HBrwaf5G5P/zDP+wz0IbWOJivX7+OxWIR3377bXE+beezrrG94v+ac+z+ZjvJ\nj6vPIob1ySQaXGXCvIMhCRwwbuzgfD6Pm5ubncACujGPLSJK8NrnwjqpkbFjHi92xnbF9og+r9fr\nguNsC6w77NgwrxwVxhgc1GBs0JE20bHoRo/F5aJeImT90DRNwUYEkNkngPEZM+dg0cePH+Pk5KRU\nWtBHJ1E4393rom9ubuL09DQuLy8L3Vj6RdWkMbQDQPt+LJNOrhhvZSxuJ7RthyPX7Bft1YWWty9+\n+6td/0qoVLZpmq/7vv/Fy7//UkT8X79KIzVHzzXbBoFd18UGI9j30SShd5tNsy0JsHPj73A+skJh\nMlD4RDXYSZM+IZhtO5TWGgST5mbycARdBuSxu19sm310dDSacJSgFQj3AvYw6IzNwM+KuyagjmL5\nsqM4Uvzbjpc2MtiyA0I7AIKIwUAScc2ZGhjZoN20px2MJsrWvOSIGAugvWA9A9fsTHuObNAMVryO\nlM9rmZ1MezsJfB+ire/3gePO5uFAT6fTUclsVsi+AG9UEngzHNMOWn38+DFub29H5ZYOCkFf04wN\nGdxuDkTsA74GYXzmMnlk2ooP+aMc1UbFxtLRS2d/a46CFbR5EyPMmgnkHKDGfTgBnLtHvzEgmfey\nI0VfmGv6bUBqHrJusjzZ6eQZ+CBiyB5Cc/Rc13VFzvwcfzdNU9ZXoucIFHm+oQsBITt/gGDWF+PU\nRsQomwnNMq9k3jPtPO+WdYMW+Jhs9mKxGMkZ92WQSPBwuVyW7O/YsatfODVZ9vNFO4AXb3TFd177\nRJv7+lHTzxE5bxqFVp4/gze35fJ8b1ZipwceeXp6iu+++y5ub2+jaYZz7sgCoKudxQRcERj0Tos5\nO8cFj7HDOHyQSyrRUQB4zrjruq4AZmQpYqzXR5illKyMaejMwCDb4yCQg6IAXsYHLdGh0+k0Li4u\nYrlclmySl+PYJtF3B2C9QYxpZaBvGbm/v4/r6+t4+/btaGdK9ArvNE/lIJL1WW9SyW6MeW64q8a/\n/k2frQdrl+U1Isqa6ouLi7i5uYlf/vKXRd4zYB9hy6bZkZV9zrDHnv+uBZpxiMBu8AM7wVo/RQzr\nCSMiptNprNfr+PTp0yjQksfvy7aIC4cFnofPOYuVfmIfTANsi/nJwSf42EFfNs2xg8a8wn8ErfLm\nWxlHETDJ+h9nczKZlNJaeIHlRrwTvwMcDS83zXY/CjYUopqxFkyDHpvNpswd8gMmsX5j3o1/b25u\n4uTkJGazWQk0uCoD2c+Bpcy3DnJAD/s21u3mXWML07Ft2xIQh89+o5nMpmmmEfHPRsS/o4//k6Zp\n/k5sNcVfpu+qF4Oi0+y+CEG9xor7AeHRb6NiNXDIZzBmxO7W9rXoDqAdZmia7QY/RDE8Oc4ewoAo\nAUedebfL8ey85LJCnKGIKGUvbPzDZkA4vCgmb3rgTU3cJ689yQ63Fbr/dv+tqLNCN0N7rrzeEoGh\npIByDiJF9AXDyFz4N8bAzj7vpazDY3PUiR9oZLDLfRa4Go32OR+ZTozDEfIssHbYRk5mjDOf8A1y\nQMTRSubu7i7W63XZvMRH7+y7KIdaLBYjJR0RJft2dnYW6/U65vN5FWibFpkvslHNiozPnGW0LNIP\n3uONfgBeDtgQbXS5rJ1MGw34jv8dJWU+kX9HC603ALDMc98Pa/UAw207lAbBt47mQgODwexoZpCU\nDWnN0coOAWNxwCbLMzqPo5bevXsXv/jFL0ab/thAmwYsBWiapjgNOVNlvjePA0DQE9DaEWDo7Mt8\nYoBkoAiwsFzCL+Zjt/fw8BDL5bLsuOxsnXm6abYZp/Pz81iv1zsBu3zxfpdij98/jMu6Aj1XA1J5\nXIB53qe3j5xQ03D33qjyU9ZVyFbEsBEb8mFbd3BwUBwWB7PMt84GPD8/l53SGSM24OHhoehxA0Le\n3bbbyovValV2lQSYAeROT0+Lvd5stuuhOOoEQMp4HCy0zmbcOFuhtbQGaFlW+xhkzXbw/v6+rLuL\nGMomAdrICGslczl/zbkz7mEeoGdecsE8M3/cz1q4Dx8+FD7IDil0cjvZ8cqXddw+/jMOefkkmmbA\nGOb9fW24D/DNbDYrZwF/88030ff9yInO7y2fR0QjTOB3W09ZPzAPDo7ZdjNvrgCAxmzAZv733PpM\nVTYvs0OLTvQ8sxcFn7PPAJlT+vn09FSWd2w2m7IxHBfOpumesRQ/YHvrBfCd13QyRzkIyWfW7TVa\n2kEyNuI90C9/T3+sm6EtznDTDMfikVhCHzHX2E9oQV855gwsD35wcI4qBQLX19fXZT8CqhCcYMq2\n3PNgrJLxaqbVc9fFpB3v4Ms9XmrDGA8ODspyIGOMfdffysns+34VEW/TZ//qP05bTD6LfCFcbc0j\ng7ZCMUiIGC9sNwiBcAa1Bi7ZcLdtWwyADYX7Q4mht3W2U2QHOWKI8tpg2TmyoHubegw1AJpdVWF6\nMr/5YGXuz9kVohDZaDhlnmlr5envbVAMYhxlMo0d9eI+wJ2VL8Cc8WPwoatL9A4PtwcoG6hayTho\ngKLLWU4EHv6wUc0GzAaGd5iO2fB5HbH5zk6VAYmDI8wxQMwRNdqDHufn56OF7pnH6dPh4XYrfJ83\n6XEdHBzEdDqNiO1aARwF84FlxnTwmJGDrNxMG+hvxUwfPCd2cNq2LRl8+BZlDN9ZMTrCan5kvPAZ\nbcErGENo6DmazWYFxMLTGCIMFr+9oQh0tGNm45n7YdmxTrNhhvYGItYd1nu8B36i1ImxMo6+78v6\n8yznOM/QyVnlyWS7XhFao9MODg4KwHFZ1GazjfpivIlaW2dl3oAe5iPzFeAmZ0LNa03TDKWMMQbH\nq9UqDg625/B6o4fsbB4cbM/8RI4sB/kyqDSIeungEDzVfLsky04sF3OKHGS+Rve5zV/l8nw7WOEL\n28YcGuwY2C+Xy/j8+fPOhl1uk7HCO9PptOg12oS3cTTJcpL1g8fv7u5iPp+Xyo8MUPu+L5sBeW3j\n09P2SBD67V0+neUyTZjHtt1dr1+zG9EP63p5L3qLIBltOBjMjsJkt96+fRuz2aw4pciXgWaeB2hO\neb+Dl87WOsOF039zcxOXl5cj3YOOyrxiG7D3al42ntF92XnYdfaG7zJt9/G1x980TUyn01J98M03\n30TXdYUefm/N0ay9I2NC4w76uS/ghB12MJP3PD09xd3d3aj6xtiK53xWtXVwRIx0vs9ONyawfva9\nBFl4NziY4LyxE//nHX5tn6i4QGeSScUBZXxeHseu0VQtoCMy3qjZA9sEEjTgCPrUtu2o8gu71zRN\nwU/Zh0CfsvfC8fFxkUsHtp3le3p6KssDKIPNAW7sHvrx8fExbm5uCgZbr9flmC0HbDO/IYf8b0ew\nGliN2NHded0l/pgD8f8/dW8SI+my5Xmdzz0yY3D3GHK4Qz9Keu9JqPT0qEVJSGyRWIGQetcSK0BI\nvYE9vWPbWySklnqBoDcMO1iwQ0KsWCBU25J4VaC6vHvrZmZE+BRz+MfC82ffz46bR15UBUpMiswI\n928wO3aG/xnMjJ8vyfnf1+6yf+fWdV1JDzvi0brOLUeTuMaKLpfDGmSaQVtRcjKYNPoFoxp8bjab\nYrhgYJ9v6AnCqHjivE05xhUBcPmYmYj+GMSjMPIaUd5FX80cjoz4HYB0g3uemx2pzLz0L4MUShSY\nc/et7/vi0Pd9X9ZH4cADfC3ILicGUGSB4PPsVOJ8Z8cpO4qmSTZG7rvpmZvnLTu2+4xXFmCDTLJN\n1OhDm67r4uzsLCJ2gYfbdDotB597TuFpFCjbn7fkI/+dQT/rMLxo3TKbQUL+zllMZ7I5o5A+YZC9\nlivT0BHETBMMkGXZgRhvFc8zCOpMJpNSWmMnDYPGs+FL+IP3sfbRuigDE8smjb7aaTT9cxQY2TUP\nAkJ4njciG4/H8enTp7L1vWUCEGLQAyjOJVjoS87gw6EEdMD/PN+7Jdt5bWVKXmo4I/xuB6HiuxhK\n/u3MbjabsrX8yclJFXyyo4mMzWazwoP7GuPJTrG/9+/us4OteZxZj4Wc5uraSHql4dj6/cx3Dm7w\nnTf8gXbMJ3wwn8/L8RwOIPK3y0PZcIM5QK8bOHNkB40+3d/fF1COLcBmRtQVEcyvsQL2hb+Rae+q\naODtORqoW75JTlgdCHGQw84qz8TJIOsE+Oz7vlSsLBaLePPmTaE9Y3Ulj8Ezz3XAjSOVbE8dVHUG\n9+lpeyb4+fn5jk5Fz9hJavFqWR38me9egqfZliCpfJ4DJy17yXdUP716td0tlQzm4+NjtQ6xZdNe\n6l8LA/G7++Tfbc+gt3UCPEFQwYF65gVcSNm08QtyB/1fv34dZ2dnFfY1/sOhgS+QATasycFl2zDb\nE+tY5oc+YAMzpnRFCwE6nFyW8kTETqUCWX76x5jRJcgYWd7Ly8tYLBYVbxo7Glvc39+XHcY9VjYc\nzWP0sYrO1IKpvTnQ3d1dXF9fx5s3b3YC59hMV+mtVqtYLBYxnU6r6kquc6bavGo6u798Zx62roCe\nPj6Ma0h0QOfs4O5rX42TScSCMj/vmNYCULkZOGWhZ8LJlGVHAQWZM1ZEwa0omDwTndIwX+dIEt+7\nhIBnA4Qod3XJkYEn3yMUADqirwgP61qs1FAQjiQhhBWNu6E8q4p08CzRlLEgiKZPBrqmc8RQg+6D\nnTmTj2dhsPjepcooBwCKHX7egVGxEs+8xP04BBGDYrSSNg1eAvPZgczG9nmzGdZayvB8vrha1xox\nQBaDM+aaqDfnLtFngB/jhYYZHI1G2ywx0XQUDLRnN9Sbm5u4vLzcK38GStkJ4jqyCfClHc199DUt\nmV8Hhpy5Yy1fqzTdBpX+OSBjJw8Hie8ILvHe7Khh5CO2ZcfwEXKMnNjgQF8HQ3h2BiT7ot+mmSPg\njMc0xuhhMLyWBlpTvo5M2mGiVM9rvtE9zmBAG66Bzga1OJQAO3jbBpF7vYN4jlzzzMxH+b2MOwM/\nrrWjseOM9UOgj/WZBHQMKKAxgAi67Wsei52NiNh7vIPBSx4rv7eyKB5zkaN+u7ykAh17+pr1v+k0\nGg0VOjggnkt44ePHj+WcSuaFvvATEcXBA1xy2Dz8iizAS9D97u6ubIoCj6IfvKYMPiIDapohNxxr\nkPVQdgIiyalpNdAsKsfSVLbd9fscwHJAg/4a4LGT+PX1ddkwy9kh3uOgcp5b+JbqJwNWwDTPYrzo\nWio4HGxpOWQZqu1z2gjy+LqMu6ChaQ4uMe1zQ1+wX8HNzU388MMPcXt7WzmYGUPuczz9XDtM+8B2\nzvRCt4goOtF4A173MVd22HAwCdQxPsugK2XYc4HjNcxnYAP6Rpbcz6Vv2DWyc7wPGUNeKa/Ny9wI\nhHm8Ljk3jnPlnddqG6ObpvAxOJtA5Wq1ik+fPsX19XXBSczb69evS1VhXnLjXfaxhwRk4Q8ncpCX\n09PTqiyf5szfZrOJ+Xwe5+fn1Z4iXLfZbMp55FQQzGazUk6Lc2nc9SU8tY+PfX/2m/zch4eHktmd\nTqcl0IQue6l9FU5m13VlnUFrgpyB5O+IdilKBiVWGDhkzmZlRcH/dmxcx+3nU6Kz46SLVbgAACAA\nSURBVDDEoPA4X86pdCImniAmEqNiZ9UKH2dhvV5XYMVR1mwgaM5W0ofsfHGdhbiMMQaF6THmObDC\n5m/6aeVKPyOGM6nYSt7PAJCPRtvSZWiNEnLkPO+ICdg0CDcdDNCtPHE4WvxV/Z1o0hLmwlshqNFt\ny/S6z7/vWOPtyyr6GTCi1AEIZFDgMTK/OKOtCDygGfpYMRHN/PjxY9zf3zfvzwEcz5m/hz8d/MjP\nyp8zV3xvWXHm4e7urjhJ3GcHgXFlENR1XQlqwYM2dIeHh0XmoSs0o0SPe09OTgqAsb4BSJoOBo38\nj+Fmbs2jLXrZ8eY5NjLZkaSyAdCC7FAaSCmR5SYiytmFLje07KCzLGMRUW28lQ/bpkqBkiQ7k3a6\nyICTkTdNW+Dejc8MAgy8TUeqKmowW/M4WaOLi4uSUYIf6C/8zbobjoDILWcmW+CA/x24qZzRJGd8\nb9pkuuQA6r7WR1Qbm9AP952WqwXszHVdVw6Odx8NyJ0lNJiJGM6hw1l0dg5+u7u7K5VGeXdIbAEA\n2FF620WA8/n5ealWcPABOc9ymOfJn3ddV0pBW/OV5dxLVrjOwVHkhGw6GciTk5Ny5Ab0JvCI/bKT\nDr6yHTR/+EgXOxl8T/9ZpjObzSpHM1+3/T8SR33mMyXSc7PeG3ij/i7Ljd+Zm6t8lstl/M3f/E0s\nFosyXjvSWa6aOEh9gEbmB64bxtpXWMfPAWsw/8ZtYEc78SQWyGDaucVGY69ns1kJ1lNh574xDjut\nvM+JCcsrWXWvETw5OSm6fTweF9vpACs/2XklIOegJfLOM0goGPcZ06ELaHwHFnJWDltpPYyc+Hvo\nYOwE5oiIKvtp3df3fbVBmZe7GWc+Pz/Hhw8f4s2bN4V+vMO4YLPZHitHqbqXrZAdLkmMVE2XZSnz\n+L7mvliusctXV1dxf38f33//fVn7/lL7KpxMCOsNGiJ2M2H5/5ZScTTK13mdXlYkCIAFH2cmol6k\nj/HK0SAEwQwOI+St/X0vRsAlKXZoUUCO9KI4cukbYzYTGawyTqJXvBOjGP2uIczKMgN2K08UAc9w\nOYsbfcF4YwQ2m01Z/P709FStH0BRo2CYi8lkUs6Moo/0zwEFngmtuM5zm7NJ0NnAw3zz+YU7mbLM\nX35m6d9AjCq72UdE13Bss3KgP2z0g+KHj4lus14gR5uc7bECB2wcHR3FYrGI+Xxe9SOP39F4jzX3\nOZfz2THK4DQDFjtqRPrgH9ZdWb4cdMgg3cDOPOpsL89nxz7ePxoNG/hgcB2ocQYTfnUmDZ1gemKk\nccbs9Dp6a9lpg7C+MjgYaN7rte3wO5/hOMEPVF6QITLAf35+jslkEoeHh2U3Vd7B2CmBxwhan7lv\n6FJobicBQ5/5zNe1Iqh2ZLw+qMW7bi3j63uXy2UptXv16lWhnfU1zvPZ2Vm1ts4NgLHr8LXL5c2/\nnv/cRwfRuC5neApt09uyPst9aF4XAxjJMhyxPbeXDWOgU6aDj+ay7KK77JCip5xpcPkY8snPbDYr\nTtBmsylZHNtb6DGbzWI6nUbEgAXYC8CVRYU/P4/BQHWHf/R3DiJkPZjtJ3TGMcJWe2+Cvu8Lf2Ez\nI6JkcNBHOAXWIa0sqn/P5fseH31cr9fVeYrmTztiAzl2sQPs1nW7fOf7ts+NiKj3xCjfibaZRxnP\n8fFxLBaL+PHHH+Py8rIK6rV06T4w3hoXegm6erz01diIz4wBzb8EANlIDP1LQIyjezL2c2YaPePs\nJtcad7kk15l/B1bNt1QagGtZMkIQE5vKexh3rnBBzrwkzHabuSA7SmCXPrps1QFnb8KT5coykBMw\nxhrYe5+ywNppcCk/xvcRUc3VdDot+Ms40rzkSiiPJSLKzrtd15U15tPptDzHeAaes072eKBn5mWP\nHT2bZdV6jD5Sbv7b3/62lDTva1+FkxkRJeJAM0Ey6PRg92UzYeTNZrPjuNoRIWJAG4/HxSD5esA3\nTOXnZwcCpcKk5Z0Nc8SVCA9jNKizg0S0CWbC8JD98xbPAGk7lY7I5AxmyaglGtqw0Ddn+EzL4pR+\ndpwMbqxA+IxxuITZQhUxnNVp5UHJKE7g/f19UX4GHoBmBM1HlmSDDrD2mCvh3ON4w5/FgAiEtFpL\n2OnT51+q8jXzk5126EXZNdFDriei7W3+cz9wNMy/lBU9P2+30aZM27TynDfHkH6HvubrTAsr/ey0\n85kdHRtIG1IbRYJBOLguV8k8YN5k7REg2GCX57KW1WVJ5+fnRZbpE/QlgHZwcFDWk0TUCj6DMINm\nyxDj48eG3NnSiCGbB/1wkLzexvSg6iIbL74nW2vQy7gAHAZAXqPOOM0j8GgGQ4AVZDJi2LIfcNLi\nNWhhx6Ylc9kO+LNWo2zJZVnoFut6HBQ21HKzDvW7tv0b1G8G/jsliUnH2PZU76t+k2w0R7ibvfHn\n7g/XmG+Q7dFoVDKYBjoOoAA+fSwXz+Q9BBgcDMWB8rXoPe9wy86xyCmlzE9PT6X6yM8lwGMnK+se\nxtHS/5lm2Tbua1m+sVGuWKL8nr7CO8g1ASKW9WRbMRqNisxwJp8dZgfK3VcHs7Nz5EDC+fl5mc+s\nT80rnjNR7fPnznrWDun23u4zpBgwWw5gtxxM2uHhYSyXy/jpp5/i559/rq7fx9v7HM2WHbVNgUbZ\nbmQ84fVu1ungNjZbo43H42JzvE6TYFffD2eiggvAvrnvXdeVIzGQ2xzscHaV77Ku5DOcUe6Bnnkp\nVUQUG0E/XfWCvWW80MqJDHiScdgHwNmzE+rgm+eD5+W5t03FBjo5BbYChzMeSm+z88p34LQWvy6X\ny+i6Lt6/f1/66OoflpZcX1/H+fl5WYdtfeXlZJ5n/2R85GuYJ/imxTOmAUsafvzxx/j+++/jpfZV\nOJlmkIjaaHrSrGQMxLKjYzAJ4IE4LQfIE4vgWqEjQHZYXfLmjBeOE0rABopn8bn7CnBivBg/nJ+I\neqfZrutKyQzZFgwUjA89MOzQxMZtX5TDLSvCPAfZgYyIssFEVk7MI0oKQB4RZbE1wkh20RE/fgck\nAPbu7+9LHXuunadvud/0hWZw3ff1bpOgwJbBzP+/1LLQ81kLpEBbRyQNBriPNY8+p5G1Fc68u6FQ\nPA4yyuPxuGyJvk/+3M/sXBZZ03etaK7HYdowb54zHGDv3skai1aWx3Ry2ZwBuw0NPIQj4yyk9ZIz\nIIA7yxeG0jvTAQRYA8vBygRUcFDgda87M91bvJTnAeNu8Oj1n4wfvWiARDktxpPn4Xwip9znjQfg\nExwH7jV4gecihnMi7bBaFzhy77H5/eZhN+i5T84zr0In6/DsZEVE2Vb+/fv3ZS7Nc/Tl9evXMZ1O\ny66A++asfkf9PbRpXpue5XG39fhuCfC+lp/Z0vf0if8932xKY5AdMWTlXeaGnGNTLbNcy3En2EFK\n8rJeotyNygDkDBtJtQABp9FoVOwmh6Ujj15y4f4YjO5QuPeu61unfh+dPZ+Wz1evXpVdw3kedpzP\nCAzSR9ZIGkDDz69ebc/eIwhNFiaXACPjLVzgJUF2LCK2/H57e1syri189vfR8qPQYy2Hz8EBf/bp\n06f48ccfdxzh/NyXfrK+jaiXcmXZ5nPLJXNom+uEAsEFn4sZEaVkkuCgn4uezWslGT9OI2WzZOXo\nY3aCHEign14fia2joofrsNe2Bw5E8RwftQc/YfN4VnZuoJ2Djsbb6F5wNDrDexGQaYVW9JMxEtRk\n/OAAAj0EYKEDfTk+Pi4VLl565HFRFYEP4eANQbX5fB4XFxfVnDE+ssXX19fx3XffFV3FmKEjdG3J\nhHGW6Qsv5c1CW3bQ+gJHk8DNvvbVOJl2yFpANv8eOpOKewxqYDyYvBA+Abacxnc2FcWbz+NzuUsG\n0blEITuLFrrRaFQpcY/Dztlmsyk7idrBzDtBwtDOMhhoWogx1DaKpr1p7dJGM6od/JxlyfPi63Jk\nHhoRfWauFotFEUiMryOAjrKR2eq6bflTXkNop95rOa38MQDcS3YXocrz5Pt26NYNwQxf2wJtLSet\nRS9fY8P2/Lzdsp/DgtfrdfT91vmcTCZxfn5e3W+atMAOW2fb2ORxZxq8BOgNSnKkz8/1j+fJtMUw\n3d7eFgckl3Yx187k27ijvJlXAhWWcZ5jQ04ZPWVDZEmIVmJMuq4rYJusIWv10A3O4hhw5/Gazi0H\ngP9bdKSf3uzEOsHOGnoL54wf9BagxJnZ2WwWEQMAQf74G77C6YuIEkyk3MiBIACw54/5YWwGZJnX\nDDpyBsY6KsueaZcNa6b1arUqWWuyY3ZocWJOTk7KetL8DNu5bPPyj524SvZilxesi77kSLbsas1P\nEaPRbn/yu0xX1mkxP8gNTg3yZL2SSwaRQxxMgB1zD09aJumbN5SDb7CVdgYICmMnAYnchxxnxyHL\n4TBHqPuajtluZF5zkBnnmCoSxkOmknsIarGOC91lXiITiuzyHYEiSoG5zs4Bc5EdzuxgG4g7EJR5\nue8/V+Z0rQqfOoPZ4tN9vJvtWaavr18sFmUn2X0Yxc9pBWv2Ae7Mxy3Zsq1xVVtEvREY9gr9BV/c\n3t7GcrmsNqPhuTkg4mUQDhwQoOEIPPQ+14AzbGexD/CEy6iNJ+3M8T6+c0UZsugTBVjiE1FjNI/J\ndhiZfEl/+XpXUFhvoaf5gZd99iPjsj5hnNAeW8gGeT6/3Buj+V3mCevHh4eHWCwWZRd24zz22WB/\nAPawgR62m7nizRi3ksuET1uBG/M7c+bEmZNC+9pX4WRGtHc/e1nBk1waQBMCYFCTHSATPKJeCGxn\n1NlKR9jtqDmi5EgAAMjZAhRHjvx77aEFiX5g/BwBQghhKJgMpWRamD7OENVgf/dYDjOjAZ2dG0cN\nrfjsIFuJZocMumYg5XUFbAJFXzD+nic7Sn2/XY/IusKspFr0MD+gTFCoGVhl45Q/c6Q3W08DgRxx\nNV+ap7IiyPMET1LiOZ1OCwBhLnxtfie0ILJ9cHAQV1dX1boo9x8a2Wnx3L5k7FvZzHyN58djJRKJ\nzLkcD37GcDhrb+Pv9UOmryOLLZ7AKEUMEUz6gWHhfUSJzZ84mqvVqtpVOZcQW17QG3muWzzI/cy5\n6ZyDT8hQRBRA4vUw6A7AEDsTQjvWhERsM8lsjGBZsONkgGx59xIGR5UJyFlHEdVnrjM/mtfQ/ZlO\nXO853cer+xpjuLq6Klkn+muARok56/zcsjz786xHLDMZSLVayxndFhvWQOzl8W4Djn4NTlTWhX7m\n4+Nwpl+2SxH1Ds3mbSLirv7hWYA6A0brbQNiO4zoi4ihXJz1g4Ddvq+zn9ibQoXPzzBAzQB3sA81\n4O263QoPP9eAzYFPdo/HwTg6OiqHtxsbTCaTOD09jcvLyyqQToPmOJjWadg3dlZ1JZWdB/SlHSDz\nmN/njRozn3ZdV+0xUOuumjYv8bi/26le6OrqNLfNZhM//vhjKSe0o9l69kt2EhBPx1vYyM/z78wT\nfJ6dVOtneB7Hw8ciWcYdILAds3N1f39fdl7P9xgj2GmC38ybYDJjIi9F4odAoZ9pjM1GN+gI5N/0\nNS7JOMk02Ke7rSNI5LDEIc8rNtCVBNgjB8xcwWg/wfyCznJCCFoaX3itd8bplESz9Ma0xs7M5/P4\n7rvvKn6FVxh79qcyXs/83rreusO41fehM15qX5WTacORo/tmqFbUv+VgOguRgboJyHdWANR1o9Ds\nNNEHIh3uM0YMwaG8IWIAwwZhMDoMbqcLpXB8fFzWdJ2cnFQT7OtbjlELrGfnoaXcs1G1E2xm5fdc\nHgGt7Nj5Hd4ZDWCGscK4Hx8fl91RKY10htKRFWhMRGe9XsfT01NRGgaXLoN2lpb+AXS8WyjXZ5Bo\n/gLQ5ZZ5N9O6dS3Kyc3BiTx/8MLT01OcnZ3FeLw9n+7Dhw8RETulex6/o313d3cxn893nEEr9TwG\nj2+f8rcjtG8M7hPKks+ImPV9XzZE4Bnc4xJx5tCyjnzCLxheng9fcK+fjywCcrzrKk4jRt5OKMaF\nnVxns1mhBfrDBpe5t57JsmO650yJn2FwjmMJTaEndES+eT60pnwax55yqc1mU3Y4xHCSqXVmz8bV\n9IwYHHDKmfmcMzgdSLNepj+ea9MjA16a9fQ+Xs18nJ8DbT5+/FjKlmwnuN5raXLb5+i17FsOkO27\npyVzffq/dd1uP7It2FYM2TYbPNEoy/QYmVP4AT3jLIvLpJERSvespw3szEusM4T/WBsFn9/e3sZq\ntdrhGwA4stDK1jnwkfFCK9DKfGVnJeMNzyt0ccmsd5vOgBpHGsCPU52XU3inesbmyg7rKKqiqCRy\nXy3HzrRZBzFfbADS97v8UtvG/YGSzwQrz27xvEv69MKmDDw9bfexcPDX8med6XsdiDQ/9H29Szx2\nxXOEjbSD4N3PM08gB2CPvCTES754J7bINsuBHZ6zXC6rdXZeNsJ4cBZdKWe7QD/BqsM01QEXHFEH\n3tDt4Ll3794VzEWDJ3fZYMiqu6rJc+GqhpyA4brj4+M4OzsrGBPsiW1kXM4esrabv6lgiRiCKpZ1\nY4j1el2Wxziwbd4goNR1XVW6jMz7iBPklKoBeMLHn0B/7ytiPstY6yVbwu+mj+e65U+91L4KJ9PC\naYJ4cFlp29AYwOZ65+H/UfR9zRSOJgISI6JiPL/DjhcAks+dvYjYXfzNc3iflReGjhIXAOb9/X0s\nl8tyfhdGgHUQMF7EsL1yHtM+gOpxtcBWdijdbzuWVg4GfYzVWRLuQejyRkQot67ryroaxggtUYg8\nz8DfTsVmsykZENbcWNCzs5Ppk6OlWbBMpzL+aJSKRt26LWKrjG7LQWjNSctBpU8oLTJ60GQ+nxdg\nklsGHl3XlfOkiGrmH9PJcur+ZHDu/ubMHXxipUbfrLwtZ+v1uuIBopXMG4DIQRjk2ZsmcD0g1SWj\nHo+j+wA4MiMRwyYi8IN5jPHSX0CrQUV2Tlzi+6WgBO80f1m/OZPpMSBn8AW60xHLUjbeDetG2WQL\nIMQ4bNxxHOzQZVBu3YFTYDBMRNzVJPSFTCvPs9xY3+yjmfnNDuc+fs6t67a7a15eXsabN2/KxkX0\ng3nf17IObr3HjlyrD61nZX2R9Usel+XPrf6ufg9zZrBMxtHPYUMeZzuYO+SMeXV2LW9UYj3goCe8\nS9AVYIj+cxktck8gBZ6hBM1VGYzTWQtslTMydhCss/O80G/b/BYIxL5z6HrEsHuvdQSyvFqt4u7u\nrjic0NAOO/TGRtJfxkefyPJxRApzlMdhgJx50E5u97nKzFggemOgOouZ5TRLbd/X+KPIrK9t8Djj\nzDKW9a11tq+1nXBfM15ovddOWa50yA1blfVXPq8RHjePWkdQJXB/f1/4I1eKMSfWp7YTPB/54PlO\n4pg37GQy5lw9Y3rAy9hVN+SQ/20PzLs8LwfCnWBhDo0Nnp+fY7FYlIoL5sRVTyQ50Gfe54IMLHzi\nzDGfQQcCZmxClv0AJ3t4lnmMRAnXOSBL0oXSes+BbWLGaC0MZ/zvhn7Iz2rZrpwM2eHvF7/9/7B5\noFmIbVgYUCYoitWgoXaAIiJ21y61gLAVh0GewaNLslwKa2PjCJLfkbN+/i73j+d6d1juhRFcSmtG\n8phQJG4IRQZcBhSZuXK0l89g0U7XoGAN2m1U+WHtCQABgc/OF8qAeSH65Pm2wGEsV6tVyY44WgU/\n5bITPofuvt48k3nWrXweUTYQ6ocvt//FbiQ1R09bisHv9G5tNjAop9lsVs5rys3G4PXr1zGfz2Ox\nWDQBeksRtZzLbEjzsww86UPmWStrjCefU5JiJ8zrk2wM+Z8S1hwIstODccm8BI8CFCKiRGU5HgAa\nWl8AuAgcYVi8q6UdCRqftwBrq2XZNG/A/zbW8LX1JxFYv8tBmYitLuOgaebLWQ9vQ+9yZRu3/A6e\nzdwBCKCBs5l5p8VMm5bcZL4zD7fkrsXfOStqel9dXRUZA4x4F+yXHE2etX1sPdfmySqLaT5ogFX6\nax39koP6kqM56Jv6M3jWfOElDdbtXrJi23d8fByPj9sjGm5ubkrkP6LOtNuBMxjuuq5s5pOdVq5j\nzbNLEE1TZ0aQgazbhzlq6zXf72BHS1dz6z46Rwx6xcclOGjoQBYllMw1GAAb4LNDaS7J5H2WHzaQ\nIzucA0OMJ1dkIY9UOZhXosPB7AovlSF323WahU5Jz2XedNAw4ov50KplWwVmdBA9X5fxkPtkXmvN\nadfV53O3sAJ8l5dKRQxnCjtg5d28jTmZf7AOma7KIdeYbde8ttJ2mO9cFYhusbOY6YF+QFa5FruD\njSb4MZlMKlpitxwUbeHW/Du2hx8HeKm8YfkCVQxUMhD4jRjOGGX+np6equAXmBPM5IoAnkW/+r4v\nu6tTZZHphJ2Dt+n7aLQt1+V8ZuSW71erVbx//77C/JTMwhO2Adn+vYTvWnLP3/vs5Evtq3Eym46L\nHL1sZA0mqavOHnfLcYNwjkKamBmoGQwCIPixM+LfaTZOfocVuI01golgu7SBowfcHxidcWJYfAA6\nY91HEytw04a+7qOjadV1w6L+4lDtcZ7sQFjBMx6XLSL8KFXzCcrTkfIMPrnWO/JNJpMi1DzHitVt\nn0OSAawuqrIHvvfzhzvgxXRqZS6yA2aA4HMZcaK8sxoKNoN0j2c0GlU7l9lx9/uhlaOFLYPs3zP/\n+D5Hdh3h53/zh+WAw92RKTIm7DoJ/0CLvOawBZ4oQ/G4cZagDf3AAHjrckeiMRo+5oi+sjYEmjiD\n7DFn2WjR258ZwPO/o8oABuYSvWEDSjm6jR90fnp6iuVyWTYboEyW96FvAPtev8Ln1pPue9ajrFGH\nD+Bfg60cYY0YKg92jWdERO1c+ic7wub7zP9ugL1Pnz7FwcFBqSxhrHm+8rzlzywj1nHOYkQIWDd0\ncmscfmYLSOd+Df2zRq9l2fd7x0VnBNHh1qkOipJd9I6GfkcugeY+l5NS7g4vUEaPTAJova6SftiR\nddCnNUc7tk5zFBHRjUbR9UNw0rhlaHVwI88Dz0THgDVWq1XRKVlfQm/v+OmG0+m16uhROxYuvcTh\nZ6d22x90B38D3nlWXrcGLmCUlR/ZDQ5moXnf71T+5ObMTZ6rPC+txnhw3HNW1vSNGGyZM3K52d5B\nX3jSjp6xBjzl91t+2aQJ3kcnOnjKHODMcH5m6708Hx5rbYbjMnbkxgkRr+NFB3v+eK4zpS29auf7\n+Pi4CibyLPqQsUELk2a9ixxab/pe77DsteIcp+XzMRmLnX76w5izA+igEHMZsXU0XVVnvIetczXm\naLRdvnR9fR0XFxeFVxxkwi9ANqGZ8UNly7a/FHq0eI9nfUmWTNeX2lfjZNK6bsj4mIGzkwYxvR0z\n37cYa/h+W85hwxHRjlxmgASQcTkK9xL1MND1uy3A7ht/YwgweB4/C/i9nsV9JELFluxHR0dV3wyo\nWiWiXEd/eK4/z86G17j4OjvPdmD53uCDeYSe0M9CN51OC/35vFWm4Mh3pjd9QtjZfbY1tkyTFn+O\nRtvMbd+IdPq6Chzq/xbYy0Lua/wcDA7OCpvKZMcO5XZ/fx9nZ2dN4wjdKL/KZah+dzYq7rPHsu9/\nv5PnZvq0QILnldIh5hQlTrkX2SScSxzNXPqcaeX5stK3/GNAMUTOZGKQuRZ+BsgCisl6Il8YOzYd\nMY330cN/Qwf/bWPPWDGoObILKGfc7KwHDdgs4ebmpuwci/xl52CxWEREVHry+Pi4yOKXnDbrApcJ\n2qG0/spGND8/O0N8l51Kf5bp25JP881otN3l+OPHj/Htt99W62d21o012vY5u9kN67EWrfIzsm5u\nOUv5ujKuGNzJWhbLnaVfmRddfg4oYewtQM247u7uquxlxHD0DTbCvOXSaT5DtgHqo9GwztDl1ryT\nnW8dyHAZvAObDvzYRvefAxZ2Bvu+L95TF7vZZD8781PoesuhHRuCOe4P80EjI8TYCCIZuJsG1n/o\nAvrnjZbIvtC/zAOuhIkYZB/bar6rWKfrov5Tsgotd3RfXfXB5d3AvPFSy7KeHW1/b1kx/Vxu7Xs9\nBmOV7Ehkp8f86XklYMxnzIHlCpuz2WxisVjEcrnc2STR/IZ+JajicZeS5qjLWnPwnuy6bW/Wy7ly\nzU4ZTh1BQ/5njDwDe2VHJ1eaGefQh2xnbIvt2JrHeT74m/0VRqNt+TpBbds7Gu+1U57X3joAfXt7\nW64jCGZcbvkyrfn+/fv3hQfv7+/j9vY2Tk9PC06BTi8FYqLvSyKoJRdc7+/N39G490vtq3EyEejR\naBTPCVQzifwOg3tL4QyiX/KuW46oJ8aRQpjS6wNhAN5rBUJ2iagH47KxzaA9R0BGo1HZXIRoL8yK\nMc7OqsFxjjBlx4F+Gxh4HmgZKJiufG4lnOnuuXKGNjugdsQ9lr7vy7bN0DLTHeGC/vTJBsljJRuG\nQsl9aEW8syMSsYVem/RdpoODGF3XVVGkfQC2XNvXGU4cB8p+iXY76sVceNyUXLjUmgZI4IymHAEz\nTbM87XMiW997rhkfc8HfzoL4fT6vkeOFULSASsaOcWYtC4alVbbIWPnOAAJ+jagdHYzDbDaLs7Oz\nAmS9rpVoMk4vNOZ+AzMbnOxc+u/MF24tPeYMIu+DZ5ztoB8uW4oY1rViaA0sHh8fyxoTHGkCfZS6\n8q6u6yqHO4MzAywDI3Z3zs5jDpIZwPmaDA5bvPkSAGzxesugMk/L5bKAAI6cALDn1rJLtnXwUVUW\n2JC/Vssy+UvucR9awKR2POsAGXax5Rhbb8KPt7e35RB47CiOjjf6iYjCS7Yhdj495r7vSwDIu0Xy\nToM1+ukNpww8Lfd2NLI95d3FoYy6qsDBuSyjvpeqC/QWpY7MP+PAcTw6OipjpM+uJHAmw7oGurbW\nLDuQzrVkSqbTaQmk0W/bNXSgecLr/3Mg7BfxcbSCk9HkLf++7/nZFlnW83dZ+hML4QAAIABJREFU\n1rEf0MX60xlbYxNa1k25j86YMV+sTUZ/wtPmQ/5/ft6uMVyv15Xz7Pc4CJP1qfUvcuagnq/BJnij\nGZoTDg7AZExNNhA6gr+4h764VNZj5h15jixH4CHbfs8DskvfXL1k3tlstuXjNzc35TondExP6wYC\n0LYtTppxPBxrqTOOh55dN2waCvbh9ADmB4zjEnjTLcvQPqfQNDWN9t2bZehL7atxMitl/HlizGBe\nKOvIRQal+0DvVhFslVV2mloKy8IFY+WMSL6WZ7WcNi8gBty6Dyhor8PAGI/H41JW0HVd2cQAhxZh\n4m+UH8oRxWFHxP3PzWPxtVY+/jw7lja+LUDXomFEXQJtoHt4eFjOHTSvoJzoc46+YZgd3XPWBkfT\nfUAB2mGzAvR87QNzGYwV5ypibznQPgUQMRwwTmkoOzBmsNAy6AD9+Xy+00fWXCwWi5LFcpma53Ff\nf7OR39dacga4y4reP16HhCzQb+/Cl4MyBoiOiFom4Glkjg00GLOPE7BTeHp6GrPZrPTNWdPVahUR\nw9FDzAuAwQ4pBtU02ScbfOf/M30tt4yb/hP15ufw8LBUCeA82gBbxyBHOM+z2SweH4djJtBd6DQ2\nwiEIgFzSHMRzoMsOH3NoHZ8NanbCXDJlPm3x7L6f3CwH+XnmH9ZnshFQ3w9lUq3GmLPutAOT9UvW\nU1GOG2mvEy/Xxi6Y/pLu38eHXIOOjhhAkYNGXMeYWCd4c3NT6S0A3NHRUalIQO4tGz4ODLrn4LPB\nF9e6ygN+o4/YVZ4fUQMuZBbbar1kJ9Q2wvSxU2LnE+xiEAwwxil2AI1jEaAB37FjZkSUADWOp51p\n20U2SjJP03wf71ksFrHZbMqZr9YxzK/1t+1S5t8W6G39n3mO9zhw/Pmi+FLL8pBtWguTWD6yPXTQ\n0Gspc7+t0/gxHfImjdzjMlkyf3ZeoTV7L9zc3OwsJ4KfCEoYV9n5oz/YBxo4l8BHnhueR9UAy0BM\nG+5xmbYDOLYLBCWQo+zIIYeMH1zOeHmf7U1LT7svpj3/QwPsISXI2CEqyNxHz7dxp4NX2FavD4ef\nJpNJeX7mV9Zn8w4qinxmPMt88BlycDo7nC1Zs77aZ2u4Bj21Dxe22i9yMruu+88j4t+OiJ/7vv9X\nPn/2JiL+m4j4dUT8HxHxj/q+v+q2vftPI+LfioibiPj3+r7/377w/CpKQXMk0+sLvGB730CzQ7D9\nfXiugXlEDVhgMkdGaL7OW9T7UFmX3nBPVpSO5GKkcTIBu4zz7OysrCXMhgulkgGX6Wol5oxRKxrR\nchj5PDuZ+8CZjVBWbPzgRCJ0duAROq+zwthTpsyzXT7HPTgIOGU80+Pv+76c8Qdd+9g1onlMOSL4\n/6S1nmnwmgUfBxuDxPo598Vz5s8NZgjQtNrNzU1xjGwE848B1j6FlYGFDajnINODfkbsyibXwCs4\nPcvlsnxPWTC6wbxlQMpzsnxEbDMnnAHJNRh5l7FjjBgngSD6gCNs55Q5dmCIZ2Wae8wZzPF7lvUc\n3c0g3CDY60vZYn29Xsf19XUxHF7bS8YH2SIww3PJ3FFWZP3M2OxYR9RZHpcsohccRMAJyPbBfG5Z\nyBHcloy1gl775DnTvPX9eDyu1meenp7GyclJ08nMfePv7Chlo994c0S0n2We6rquVE80n9X3TbDO\nx/y/ff4wBpwjz537EjE4dev1uuzoGFGfmUl24+DgIM7OznZAkXW+S0DN32weRH8cxHUGymf2ATg5\nBsC2MGdGyEygKzw/zhJCGwdL8nz4x3zo79FfYAMynTc3N4WmXMt1ng94yDvMWgflYK77moNBHIPR\ndV1cXFxUutmAls/pu3eOf4mXXd3T4lHLHmMsfXxRPnaf4XF6zvbpAfpsWjNf1qdZt9mh8zw7EOHg\nNz9PT8PRPdCihVVZIkPps8fFddgsJx14Hw4JjqzPTSZwyFyafsgQOjkiyo7E2EfkhnuwF67SM+/Q\nf763c07wFx2Qs/KmHfS2zNn+YbeNifNGocyfExwETJkbKgmdiYQHslyBLT1GZHSz2ZSAG04jdLff\ngy3kGcvlMs7Ozqr1m57XHChqyly/G2jJtjXLDmOiysJy8SUs/Eszmf9FRPxnEfEv9Nk/iYj/se/7\nf9p13T/5/Pd/HBH/ZkT8y59//rWI+Gef//9ie0lQHBXYbDaVwXDLxLWC57ktR8pAFsbxFs48w6Dd\ni7EBjggw7+Z9VlDOGDAWIuIoLpTSdDqtSvOc+QGQwWwIZe6PnREMl42JjYRpxu+ZkVqO6WbTR0QN\ngC0sdmodweVaGJhrXPZA+dNoNCqRaeYiOydEkfwM6JDB+9PTUylBcCTvJUcTRbojkN3uOpPKiU9g\nD+OanQYbIZS/s9oRUc2fx5Rloeu6ckbUbDbb+e7paXuGGDRqjTk7kvye++3P+Wxfn3ytjUR2KOEV\nDA2OkQ8Zt8H1Jj9+vrdl55l8Nx6PYzqdVpUCNq78vdlsStbb2RSDQN9Hmc10Oi2RYpfLugzOjpuz\nLXYsM39YZnPgg+/smLlsCFqQyUGuAKzoIhx3085ljwRzTk9PYzqdlkO/6ZONntfWemw2+jQH4txn\nR7YNljxm69dMtxbAz7zcoi99yrbEPA0vfvjwIV6/fh3T6bScG+jWMsiOgPOuFkjIgLzVsjOU7WAL\ndFSfld+3mVIcTI8d2mWHh2fhEEZE5WCiZ3w25sHBQSm/NnD3Jne2IfTB66zY8C5iqxtd+o0jiXwZ\n0MJ3/J1L36Gh7fU+O5n/Nl+9ZGsjYkf3Ijfj8bgsj7i/vy+26vDwsCyDcGYBJwDg72A8gN/BMp/T\n5+yKATdtvV7HeDyON2/eFP7y/BvnPD09lfkv4+0j+i9u67NLo+wMNp3KF3ibZ2R5b82V/3ZfTBf6\nRmDRfBkRxcE2xjEdIwZs6/kh624dbPvFGOADb3qF/uA59Mnj9jjgaeaI0mjGZDoYe/d9XxIgz8/P\ncXx8XNbtRgw7TTvD5wAyz0G+vG+AdaBlgR9vLmaH0tgIW+Axg5V5vkvvc7CIPruigKoU0xOdhqOJ\nrDkZxZyjU/p+W4HmcmGcxtvb21IZ5Uor5IoSefq9Xq/j/fv3ETFkb8ETrvzJdjDbOQdEsrOdbZ9p\nzr1ZVva1X+Rk9n3/P3dd9+v08T+MiH/98+//ZUT8T7F1Mv9hRPyLfvv2/6XruvOu677v+/7Hl95h\ngiCETBDMBCNZKauPEVErhZzpyQRpZQcihk0I/BwmhOi/M0owswE/TMJkZGPg5zN5GLjHx+0Osaen\np6XELmJ3w5K+74tiytGIiNgxKs4a5CyK6eHPLGD8b4MiLqnmwiDeEXobbOgAOCQyh5L2jqnch9ID\nHDi6aVBCywbLYBSQyoJsZ/s8TgO07GSW9yQe3CkdSwaSPETmYzsklGw4cpTH4f4wJhuQ4+Pjsu7S\nre/7sv5nn3y4/3lu82ctOcqZfJp5Ncsov5vHoSfZIaoGHHl1uSZK3+U7KElnF23EcSCzkkVuAc52\n/nFA5/N5ifYbzN7c3ETXdTGZTGI2m5WjAbwGM2IIGuDkGjxYVqFnRL0Tr2lpJxLZQvZtJFzSg6PI\n+NETl5eXJYNCqSzRVpx+6HN8fFyMHeWKlFEZZPFub3rBj6PhZFDNnwYCpoH5xfy4r2Wnu8X3Lcez\n77dpvZec0tvb2/jw4UMcHh6W89n29YExea79bnrV+f9uN3DjlnmhSYvuC9nN1IZraucDoIxdo+UN\nSXLGgOcBoCKi0uV5XrCv4/G4lAliG+BxfuAddPvr16/j7OysHJviEvusdy2HzIuXY9T37Q/KtkBy\n/p7fHWiyjQT7GKT6vL/ValUqUAj4kDG6u7uLyWRSnG2ce8Zi/cJ8ufTToNO72t7e3sZ8Pi9HJxiz\n8UywGu8ptiHhg+i6IaAhfrRto1l/Zx76/EEzG+/muTAuM2YyHsu6xboUBzJXdUBr+J+kgcdE2SXv\npB88t6JRRMWPNzc3VTmtr6ds1WWargJwMMBBeKqj/L4Wfsu0NI/ymZeqOBBuBxEa5p3Zx+NxtUYx\n63S/O+t6nk0FooNg8C94yuW/6BUHL5xg6vttQOrk5KTChj7x4O7urozF+Jx3931fbBjju7u7K9le\nZGS5XMZsNiuBSQcbRqNRWdoSEWXpAfzknfEzLsh0yw1alWsbWNDy4s+QA+OSVvu7rMn8Vo7jTxHx\n7efffxURf6Prfvj82V4ncx/QjhiiHzZkBl8GixE1MV/6zISxMcN4ISgoTITTAmYD5zWXCHfe1cqK\nxpPLWkOfucchrh6jAS8/3s0qG2ZHuexsGrhm5nME1/Ozb66sHHccL80R97mUEAFkDETSyLBAK6Jj\nKCIcAgQbBwpjgHLHyTawtvNPBpl3ewOD3ExbA8J8jR2V1vetZ7acHdYvYSRs0DGCnksUN5Hvruti\ntVrFcrlsKgKMAuPJfcxKHNq2xpIdTBvkDMhfAshZB9jgMD4CDpRooujgIfMZ74QfiPTDE1zjtQ19\n3xcFDh1tDLtuOKvz5uYmrq+v49OnT+UYgbu7u5IZjdgaBDIGBuc2aug4HC/PSQ50mIcz7ey0O4Pv\n+bDTaYcE2vKe4+PjOD09LfxjXQLQoX/MAeCJAEDE4HDwrtZO4Jnn+Ay6mO+cvXR5UubXrNcyDyJH\n+653H6omkGqAylhGo1EsFou4vLyM09PTyG2fQwJ9sXM77915Rh8DC9Slsw7m5XftXp2evadvw/CH\n9UM4SI6eYzNxMA2KnTnEaeJ+Dhl3eZ/pvNlsisx5bT785gAE/6P3eA9gD72PvGEnLBvWFS+1lgPZ\nwh/ImWXQsolDibwwJgLa2L6IbRCczCJAlrJH8AZZtuPj4xIYQ1ZzsMZ6h82H7DhaXz0+Ppaz+/J+\nBhHDMU/OnrXk8KXWci5yYHdwXmOngqg1Rzlbw2dZL2TZyTbfayT53YEidCH6nmfaHjHvbhnjQT+w\nm5djcA1yxNwb/9gRte5H3sAWXn6U+dNz5yqA0WhUBTFIDvBdzu4yVqoPj46OYjKZlOUjLCEpy5ZE\nc+Y0O+uWHTt1BEO5Lyd/sClkgn1/5js+Z90kuu3Vq1exXC5LkMtLtHwkDU6vN0dD1shG8/nj42Os\n1+sSLKCvdsI5Jisiyv4T9juYJ3RslhXGlu0lPFvka4+8whfZbtbJpt3297LxT9/3fcde7L+wdV33\njyPiH0dE8d4B/oB9l0si/Dmy0gIpn/u0Q1xfk40w7yMKb+CPkrDz6EinM23OgmZn0oDRkXRA3Hq9\nLplSFISdzIgo9LEzbLpgIF0n3wIcpp1plB3yrLRfipAbSL/kfLaMLUaQcoKIwelGCHNmJoNlj4n+\n2ThnYOi+4mjgHEDX7Ci/lP3IwLTlqPlvGyAOFs8bqhiIO6CCcfH2/tCC8rHlchnL5dIg4l3Xdf9r\nRMR3331XRWwzb7Q+y4a5NZ6WoXYzyM/N8mHQx98ud3EGk/e1+ocMG1ACap1l4x6vUULJswCfjUkA\nni4FpF8PDw9ls4Dz8/NmkAPZB3gQ/TbtWrLXmpeWw2ln0/oH42CDBHh3KSDjZjMW0xUwjJ5DRyPH\nEYMTTTQYOTRozsFB3ku/0JGU0cPz0NPzm8G9+SzzbosevqflIMCbma/8Hn++2Wzi8vKSxxaZ+/77\n76u58z0OIhXa1LMdu65hnWHkfoCvbZ1p1PfbzE+ne3b5bdfhZmw5yk5zBn+9XlfBYcAoQMvrq+AV\n0+Hw8LDiPQ6Z93pF2w54GQC52WzXOnl3VN7nkk50ikv4kHEHIfuSfGPe6nk0DbPN9twwH5Z9A3n3\ng37ZWY7YljceHh6Wo7hwGGxPxuNxCc6SQcEZwTlEDuycRESpBEE3uF99v81oHh0dxWw225Ex0xid\n2HVdRBcBx+1ofoHbLnLwaVir3cQc6VGfvy8y9/bt2yqjyBzkctaXMCTyA/aj5eVUEYOz6GARc2JH\nriVz0NtjxV54gx9wFZlUKj68Q3ML8z4/P8dyuSz62sGfrC9wWnmnZdX4LQcTXKHCM109gLzRB2QN\nHoWHbfuZC9PJQRreZTtvLJoDk5SbM0/Z0TQvUFHIjudshmRbybWME9wKLW178BXIjqLjsHccBQYe\npF/wIud4Pj1tz672EX/oFI/XfG+bA40c1OC7jE8Zh4OJlqN9WI/2d3Ey/7b7XAbbdd33EfHz58//\nr4j4E133L33+rGp93//ziPjnERFv377tYRTv3uR1TjnS4N89CbQMPCzUmZlgBCsQ7oGZDZj8fJxQ\nR5ocgfd7EGicaCt63mcaWMGtVqt4fHyMs7OzODg4iPV6HQ8PD6Vcg/6wC2neJCGP15mSDLQ8vhZN\nsxBjVH1dNrBZKKGpDR1Am/VgbLhg0ARPEO3x9u3Qj2YlldcWWDBQVGxAwfb5LYcxR3LymPc5mBZq\n8yOZR95P+Qpz5OcB8JlfDAHODQvJDVySXHzs+/5fjYj43e9+11uht+bR/TYPW5b4zt/vczLNA1Z8\n8Kl/9xpZB1FGo1Ep8/VY4QEHQQCgNj7wnoEDTpU3+QIgs0Pf8/NzHB0dFd6A3l4PQX8Y6/HxcZyf\nnxd+M+87AgoN0Q3uX8uBgiaZ9/I8tZwogBX3WRfYiYB20+k0bm9v4/r6OiKGjUNwQDebYRdEMkwn\nJydVAMhyyHOhsQ1aLlNjLr3ZAH0wb1qv26kxb/MO/rej6Tkw/9vx8718x7UZpNKPT58+RUjmfv/7\n3/dZv2Z9ah6qWz/8W+me3aANvJWzcJbXVvii7ttwD9+19LrlNiLi6uoq1ut14XmAGQAMQOtAsgOu\nODUEI9brdalYcEl8Bpr8zZiPjo7i/fv38e7duxJMwoZgB6CHy2SdxXT2yeKX5dHOiK/JNGXuMlhv\n2WfsELQiI9l1XVxfXxdHhCA8O45zn3mc9+WSXPoHvyCPr15tj8pC7lyhxRxSYXRyclLpcn4seztY\nQk5lxZ8N2sLfraCUiF3u/fyOInO/+c1v+lZwOYN/47A8j9gJ+I9+QBvbdJ7hjCfNgUXrE/rC2YcO\nolCxQ+DEthCb5s/tVDio6fWSdq5cQk0fwcLe4AeaOEvp5VfQ3uudHVDEuUT3wx98zu/Gg1lHOnHh\nYAb0yzghYqhmcEYTHs90ywkldDFLN8bjbckyjrp3gOZanH2C0wR2rNOhMctJeA78QAnuZDLZwXNd\n15UNCuEXyuShn2nVkpfsvFu2sp0rUpjwne/9fzOT+d9HxL8bEf/08///nT7/j7qu+69ju+HPvP/C\neszSmYP6cGRHKewUmXAGWBnw5r/5zM/is3wYdEQdUffahIh6QS8CfHNzExFDNDmiBiAGTShysqf0\nfzKZxMnJSWEag2cynAgCRsc1/s5smVYWVn/XAlU007F1TVaY2an2nFixe768bsDlQp4Hjx2lBU0o\nR8zGE8XCMzL4zOPkPs77Q/G0nEY7DX2/ux4k82c2nFa68B5Revjd3xvEQUMc8ZbTZ6XbAs+5r9l4\nZxDkcft9WVkZiOx7lyO9zLnBFmNkvHY8Dw4OSiTRJVkZMIzH47IWlfc5i4dh9YYX3uiGOQJEsHmP\nx8pmCbyTQAFjitiulbq4uCgZB+6lMQb62ZIRzwe6oKXr4MfswLeiwQbpdgaQK5faHh0dxfn5eSyX\nS5ymOD4+LgYSumIocdYjhrWmNnjOHLGOk42bGEcG3xhvxsj9EbFDE9OlFezIxjPLt2noPvwSmcn6\n8qUjTLIzzZzsBAZiN1sT1be7DT1ZO0m7NpNnN4G7+pl1F8/kWehZzuxjDF62YDAXEWUDOwIU0Bt7\nwBicDQVMeUxUoIzH45jNZmUTt9lsFhcXFztnBFp/Y2/QQcioda9pttv245DMi33fl3X7GdfYAbRs\nECCjv2zeRqBxtVrFjz/+GNfX1xU/RQy6BZA7Ho+L05gzR8xJzrYRYLMjZH7F9rrM0WC1RQvTys5m\nLtP2HBtD5Wd0undfc6bGz3LVip1F3o/85A3leI5tS2558yreYzvAWLFr3n+BflAt441tCOLxHuNV\n7uOaxWJRMm/ZptjphWccHOz7vsJWPNtZ7YxvjDHt0MEnfE8pOgkV32Mey//nd4HBzbc4s64OcuBo\nPB5XtLYecFWi54WkCLvxox9wpKk8w/mM2Or/2WzWdKzpP7vas96Wd9/d3ZWMqLELmB/HlU2DrGug\ngfkyy6d5IMtWthcZV/hvO5372i89wuS/iu0mP++6rvshIv6T2DqX/23Xdf9BRPyfEfGPPl/+P8T2\n+JL/PbZHmPz7v+QdEQNQ8iGkBtp2ELMyUl+boMrvsLEbjUY7Soh3+DOe453qKI3LJXsGaRbQ0WhU\nRT8wZi5PMaBGSMhOIgCOwHjMNhaOMBto2ahkZzA7Q1YiGURl5yVHRmkZxDGHfE7pDgqU7KUzWSgI\n7snZKyJN0N5Otg0FAJVyjdb4eX4OKjhbWvHUZyNnRyDTOdL3KDsUEhkzL3xHYSLEjpzZINohbUVg\ncyDALSvvPOetec6GOCsjl220aGCllOXSRgJZsmE6ODgo64W43+/yDq18l+c2yzfPYh7Izjmient7\nWzmaOKQREZPJpEQunWWBL3GOXMZjWhm0mz5ZFvfN4eeZjEjHWUBLg9k893amoBvXkpU8ONju6Pvt\nt9/GaDSKy8vLas1PDpbQR4DFbDaL8XhcHZPjbMBoNCqACMejFVSkSsSlf3l9eivoYdpZ75neBtG+\njnkoz9gOIDaSgZZDEVGv+d+ZLc1tBqHZYO8A6L080A5gtAJ75bp+GyDL9jP3lWZwwfsAere3t1Ul\nhdfawx/IB8Ebr+nzUSLsXgwdAaOAuBxgo6RtOp2We5wtpw9sSkXWz/qYgAkA2LaVsRrwZvoYmPma\nFjAzPW3HAJHeiAeZYn8GdOJ4PC5rsjj70msF6YcDXhED/mEXWpe0Wl/yuwEr/XVJJLqP8fDe7NS0\nbEwGs3YXM53qz7f6rrq325GUcr1LApkL9B+2lc0Gsx7BicvAOmKQsYx56HOurspO82azKRVZeWfX\n0WhUys6RF/SecYrHxftYw0kVgG08mMLyxJyCvXi/7YfxEAEgy2iWE+SN7JqDyVxPpm/fmmhjgpyR\npNmZNc8af4P1TEMHteF3bNb5+XmpviAAytjz+maXNjvIDe9cXV1Vpe08CyebeQFfUPLMunYqFxmf\ng6qvX78uy3hYcph9gGwDLS+uIsnX5PvoUy6vhc9far90d9l/Z89X/0bj2j4i/sNf8lxa120jLwg0\nm3BY2C0oGehnBWOQlsFDC7wZHMFs+XBmDPbR0VEcHx+Xkj07Ci49yNEenCSyl0SmuNYlEI6SdN2Q\nqbQTbuaG6RwNbUVi7XSZJqaRI0Pcx49BkA1TLmGIqGu1rdTN+Cg3l0ZhMF1ihaFEkDiEGAWNkuZ3\nFBLrUaCvzyHKh2tDP5Si+QrntGUwAWr7msG0o3kuCyNajNDDUzZUBhwGOfQVBeu5Q2ZapTstmfDv\nOYNB/5hLBztofG+A3XKUMhixDDhAgjwArMj6MzZHXR2dBzjAB+ZdAj0ehwExdHC0lTGyxvLjx48F\nJNNHsnje/IAoLXrBQDU7Gt6pL9PGtHMbdGGdufRcGBzlufAcGhRkYIwcnZ2dFScgr2VxQK3vhw1+\nHDzJ+gOQg6MJjxvoIJOAMA6Fz6W4+2yBAW1udjLRe9mx9DtCeqsVKMnz1Pou99X6q8xVtLOXjTBR\n9H2dWcyOZg4yZbDPZy1HM9tb7rHeYG5ciQFNHSyw04u98K6LLo3FWUX2OILp06dPVYAR/clRAgRh\nT09PyyYZfOYAXg4w8DzbT2Q60zTTLs+zZTBfY/3sPkREtUYNGesTv6FPIrYAdrVaxWazibOzs+KU\nsCEJTuRoNOxzgGw7AOYlAHleqSpyoMxyznNub2+rfTUy9tjh2ga/9X0f0e2eW257xi1dN2x61dJr\nueEMOyNk/eNKEttXO5it/uR+OshoveOgmu+Bh3mmcRRH1HhNpHcXxqbDVzjKh4eHcX19HVdXVxFR\nL9NBl5vmtuG8w7bR9OA58Cn4DYzlNcTG8XZUcWZPTk5iMplUeDA768a2th/uD/3zT+Zf40KvXY6o\nA2DQk2DVfD6PyWRSMphgSjKcxmu813aE+eSa8/PzmE6nVSluxgs4jvDDer2Oi4uLgiMItNrpvLm5\nKWcMt3SzsV0tT32l47JNs4zwg51yxVcOYOf297Lxz9+1wawumzTDtK73/5lR86BbQs3fTm/j2MFs\nWfHOZrMSGeFZlL1GDNkyb0SAwczbOBvIj8fbdZiU+hiIOUrEd0S3uL+1/hKwb4HMguixOTrXAh/Z\nkGaH3crBitOKlnuhDbQEQEJ3yugAqihdAxe2okbgnp+fq+g177FjRLQah8BBAW+U1Pd9yap4bPvA\nauazPL8AbcZ9e3tbRfwR2rzGw/zPGLKx8rz5HgIKKImcWWk5MO53HpffZeNk2mTeyXySjUiObOa/\nczYZvo8YSus3m021WZMjuxgQDIu/d9mMHVdKbaDdeLzdlInyO+907PLu5+fnOD8/j4go64lPTk7i\n9PS0AmCeGxt3B9Wyw7ivrNF6cB+PvhSltI6jH9Augx7kZTKZRETEYrEovI0cI4t22tkABvp63nPp\nL1Fin4Nq/eVlBTj0WY8apFpOMq/b2Lofpo/nLDf4PdP2S/e1dER2el5sPDM5IFm2GFfrneabHO32\n+PK1mX+YQ+8/QHO2C5lFVjLQv76+LmvRIiJtuLOdO2/8c3CwPT/aOznSx+Pj4/j222/j5OSkHHVi\nQGe9bqcJfYEOiKiPl6mCDXoffczjz7yXg3GmsWnI7xkwc/9yuYy+32Zvv/nmm5jP50UWXr16VUCx\n7Zh5JGKrTzaf6cGRUByXZr3n5RvQC8eVMTO3yG6WrRZfZZtgWrj1fUTfOwNcO5v5Wuhdfz5k3u7u\n7irsgQPtkkRnoSxXHhdzDf0cJEYmbCsjajDO+E0v5oy5ZjmGnRDbL2i/Gk6RAAAgAElEQVRGMJQ1\n8j///HMp7ySACr9ZP6BLmTs7sF7raRyFjERsZQObiG3gOtbnm26MjQCOg/84+S0e2Mcv/t44x+uJ\nCSwwdp5NhvLp6ak6xWC9Xsfp6WlxIs/OzqLvtxtd/fTTT8UJJ1NsmcImjUajam8X49zr6+uYTqdl\nnwZviIQNYqmW8fDt7W188803RSeYp70njLOZ9KdVMut3tvC+MaUdafApCTInZF5qX4WTmYXH2YyI\n2pnMLXvp2XkyE9rB417vBuuFv3kiJ5NJER4MHWsnOXjdWRNnqrzmyMYMpe0yBZiSMTiCMRqNiuDQ\nN9aemTY2ahk82ejlyK7p99Kz8vPs2NJQHPxuh49xco+dTJ6FQ4ghI2JKNhDaw+xZsO7v70uW1BE5\n94PvvQ7CkeXMT/7e9GHeM3/lTAyKzxl7lEuem+zcZ5DsvkBDxui1CBlgu7UUtnnCir9lvD3/KKOW\njPpeOxe5JIYfywf8w6ZI1hGTyaRSlAAengEQMG+iZ3Kwgb9xHDPfo8hXq1W1xoL/meNckpN1k3mC\n3wG7/r4CrJof+pLBvx1W5Agj0Mq6ZR1pnkEnYmzRjehAANVisajKsSKi0MaOqftrGXRQx0CWSpIc\nOMPYOTruoEoGKtkJcLbCDpP1fMuht4ORdaPBZP7uS84bc0a/np6eUrZyWxbYb19Qj0lXtYB9lv/y\nneQ5y+o+kOcAl/UJ8uWMKXqVPjDPBi+Ay6en7eZI7NpsoIZOR0fiHB4cHMRsNivltfA6/HlyclL0\nOtUqp6encXx8XJ2vi9zZkfM6Lhr8CL9lwLvP8YRuxhKM3XPmLBfzYccCvWDAy74LlNJdXV3F4+Nj\nHB0dxenpaQmcQ1ecmSIDyuggaxyJ4tJbvjeNrNfsaLJZiWXOzffklm1Ulk//Xnh45x37qwfQ9+v1\nOqbTaZkbfrLt8tm8lm9noEyj09PTne9NH2yen0MVlmUKnlwul2XJGJ8V/ZB4Cd5cLBZxfX096LHk\nxJhv4QnmFPnkOgc5HJThmoODg7K7MJtqWT8SbGRdeP6BBjhG7lfGanZObQvy/EAngpTc4+yyrycB\nFDEEtd68eVP0kpMWHC0C5sCu2h57/o0bkHe+m8/n8fj4GG/evInj4+OyPIfrbm5uyrnVBN6QZ5Jc\nnlOWinn5F80YoNXge/fftgS9a6wKbRi/ndR97atwMiPqTA3Eyc0KzMrEAGPfgBFi7gP8QFBHlfzM\n8Xgc0+k0np6eYr1eV4CQSb69vd0B4y7ByICTMgELMgKJUUMYYSoEhv6gCGezWYncALQ5iiMDUWhn\nJ8XGzTT23/uMQHbAsqPvCLBpauXrOTT4Bugvl8uinBAmnEycNp6Hk4BysKOL8uB3f0ekiXscKKAf\n5hua142aFgYflFrkUlcDC8/PPj5uGVv331E1DJKDHrl5zvg/g6UM0lvPoK8AvrzBjZ9hhyAbPRsP\nnB5fjzJGcTtrRlQyIqrzwrxZgqPudp42m00pZWedH/0mq8mYfvrppxiNRrFarYoRclCIQAlZTG/Q\nwDE6jIcxwPMOUNQyyfzskL/paNIf5AkdlANFznzzP+C17/tyDI6dh9evtwfbkxHo+7467ytHTgHL\neQds8zF09vdUFZhHPKcAkww8PD4+d39avM3YoN+oYXfMz/zuvvHMljO/r2X5Ks5G2IFs27JBtiI2\nm/1ZUJ7rahg+bznMLR2Qf7dcY2ucCUb3Akhsz5+enorMwDcRg8w6WMSPQfvp6WnltKELDL5Ho1HZ\n3Xaz2VY5sB4KvU/1Qg4OZhpat5HlcxayNZe+z7xh2juo5fu9dMTY4eLiIk5PT+Py8rJs+IH+wAlg\nHA7u4Ii7soD3Wu4iouwv8fz8XOETeJrsE/SxXuB+1obto8U+B3Qfj9Ff0y4iKhnZlwFz4xqClJQW\n8kxwXtd1pTQ46ys76fAbvMEGSM4AmZd4Bp/l7B3Pff36ddze3paTA5w8sE7jOT7KYr1eR9d1xc5Q\nIt3HUJ02Ho/L2l6CLcwNcmYbgowTcOSdJycncX5+Xp3rbuxJUsB6kr7Dtw4iGe9Zthm3cbrlKmPZ\niMGx4sfrm421Tk5OShBmPB6XNd13d3fFOWfMrHsGE45GoxLMgh8Yt/FGsSlpDsk4v337NiaTSXUu\nt4MaBC9wiNlxljkCb+WKFegClm4FQu0U7/vdOyozn3YqW3ak1b4aJ9MAvKU0skH3jwfLNY4Yco+Z\nzI5F3h2NiUVZr9frsqMUbTwex3K5jNVqVTkUFloDPTKfRH7sHAGGUfAoOINcl4vO5/PYbDYlKkfk\niU2IKGlj/AaeLYcygzl/Zhqa9tnRjNjdxCIDM99PWYOdKoPhp6encg4Xa3HyOwD/RPzcb7KjGEci\nh54/R3lwFFqOt50AK3kDIY+VvqB4/A5H6jKPZx403VpOnueB/hjAIANfMsJ25jIobyl2nknDQOW+\ntfqa++JslH+QG8rofW4o5UleN+0IKaDKQQVn0XHAnSl3Js9BLmT5+vo6np+f4+zsrDij3rgAZcwi\n/7Ozs3JGH86by0/zkoAMbLLjZDpaTzIn/A5/udS774eyOcaYGyA1YitPd3d35Ugfxoue4SDto6Oj\nuL6+Lrsfcr2dinymGOCXtemAKTuafd+XsiHohcH3jo2M1wCGcVjODEQtM27QCUrzbRd1MC7LAXOx\nL1rcalkGWvJTXR8RPe/V5y3Hl9+tq5ANnjVcv31i328d1n0OJvR1dQc61hF0zpPFlkXUQTmAOEDa\nTiL2jWfzDMsYDhgZCPgoYtjg5/7+vqxHe/fuXSkhAzDRZzJFrLPOweNs21o8k+dxe8+ozBLfMwfQ\n0CDaoJgjg8AEr15td8l9//59lX1Cv+UMkXUslUAEsq6uripM4zF5PuHFyWRSHZHlbFLEsCTGQeO7\nu7tq6QI0MO0GGYyKTnBmvrZlb1q4Iv/uhhMHlmAdKs9lXDiLEUP5IvQw8EbPwbvX19fx7t270gdn\ny7KNxlHj2c62jcfjqooEHZazRfSZyhp0rGUUfn58fIzxZx7L5ep5ORBzyjvZRdxY9PT0NCKGs1TB\nHCRLONbLQaa7u7t49epVWetonOGssGXCLeP+mo/qclwwt+cHXYINAxtxPc7y5eVldYYsS7IYT66O\ni4hSmQY9cURtT5GfrquPsPv48WM8PDzE27dvC36DNxgPpfCPj4+xWCwKlrCeRH/aBtnPyTbKfG1/\nh//N817CZRuV5+Kl9tU4mRDWgpUdn5bho9lJ/dKzrfwRIBQsz4JRb25uSuQ+YtiWHiY0iIsYymsM\n6HifjUPEAH7tFGw2m3jz5k2sVqvyLjPR7e1trFarsoYTx/L8/LwYeEcxstPCewFt+yL9jDHPQeva\nPEcAdZeCZoMNM+Nw+nsUQdd18fbt21iv17FYLKqF3ERvfSYo4NgKi/b09FQOsnVZA9ehXDDmPqPM\n5V4IHgYHOhkEMF9l/OJBeNcKIQNE/xjcZscTevt+K5KXHFTPXet57luLN/y9FVY2qg4c0GxYc1/s\neMHHPiePIA0lMQApdlnDIY0YzmI1gEUpE+Cw3thsNiVCiSNKpHiz2cRkMomHh4eyKyMR2cPDw7Ij\nJhvkYHAAj3kes3OZwVSem0wrA1Wa5Z2/ndWE7o6IMw8Es+Bv+DliyMJGbNeEuSLk7du3cXh4GD//\n/HO1+RayiYHEwDobgB5EXsj8YpCdzc58Bh1MRwy7Ay0tfnUfTWcHq1raLsuC9XuWIct7bn4v9Nhs\nNvUZmKnfJXtTdMDQh5aOZ5w5AFfrkiEjOtBpf4bOzSVUzLWDfg6wRkQB8VdXV8VGsbkb8hkRFXCd\nTCYF5D4/PxfnEtvCujI2E/HB5HzvLCUZIpzMp6ftDo5935dlJ2R6chDQeoy5hcZ1MC9i62har9dB\nNZ6Hvc8l4tipg4ODkr1lHukTGw9CD3CFgfpoNCpO36tXr8qadgcBvJ8DfEFAjw2U0HGMyVVW6FR0\nMkCZMdLngQ4ExIbNez5zbGSpGwD5rvNZ7mo4/7kB4COGIMTFxUUJAI5Go1KmCC2hIf2A329ubkrm\njTlcr9ellBHs0uqD7Tk6Fh2CM4EN8lnQdjTBJCwfAeuYH+k7O6Wib51dhO9IeNjZ9v4Ufj+ZNDJ+\nLBODf2l5I03bWeYCZ7uFfeDHiKGyDNnOwVjmJz/DNsP/931fzn3GQWcs0J5sLbqEOV2v16UcFzvp\n8WFHHXwzhkbPO6jOhkJv3rwpeo05QM4Iqt/d3cVqtSoBEhp+AVU+xnuu9LCcwNPmrYzbuGYfT2e7\nt699NU4mLWeFDMLssGQDmI1hBmuO9Fm4mVgTFgFjhzcYC8UNE6EERqMhfe4InxdzO6rhc/74zO38\n/Dy++eab+MMf/hAfPnyI8XhbC85YTk5O4k//9E8jIuL6+jrOzs52srLOXvIOg7EW3VqgCFpxrYWZ\nez023m3at1pmepQcRvbo6Kis9/qTP/mTEskZjYbd8iaTSaW0+r6vwI7BLXM2nU5LlJd3obhzuQLG\nGB4wLUwvIuEoMBRFGXvf7xS+2RHIoDnTJd/j37ODCdgwwM4ylfvReq/nN8+lrzNINuA3UMtyy7j9\nvelVgL4Uc8Tuzs+5H8wXRpwfG6+u25aYQx8rWHgHmX1+fi7btM9mszLPrAcG4Np4YyCsd9ALDm4A\nwjGodpIYdyuI5jn1vNhAWD/62XltjZ1M777Jjp3oPwdHkCXzHkEYO9Z3d3cFbNIPGzX6YoPq7Azv\ndIbJQAHj3qJfdjBfcvrM0wZD/nyfMaUv1uW+9qX3GSAVA956Rwh6S+b9ftOl1ccMdGwTIrrszyan\nc3e86EYCs9DLGZkc9WbnU2+KEREV0MVeEvAggOhyT+SJsRweHsb79+9LgITSQYARASU7GhHDZidd\n15Xg1Wg0KnY+AznTnTG5ZV1XX1tn9rCXjCciilOMziJYZt2JYwww7rquZJtw8CwPOIbPz8MaQANq\nZND3ISebzaYAYDI9OeATMehq9C471rZp97Jdy/9b9/vajEfgwZdk1I7W7e1tnJ6elrFCSwdErC+g\nBWXYVGEwf+wEe3FxERHDGbAO6EEjL8dx/3Fi2V3UwTj3qe/7UlmHs8P90ARnB/1rnea5Qz5wKgmw\nYEfBWdi809PTgrmen+tj3ryO1QGgXIng0lXsATa3xQPmRwd5CDYzNmhl3EaAN9tG2wrkjdLZq6ur\nGI/HZckLtOeMWvjAffTvz8/PZaNH5NW86UAQY7m7u4sPHz7Eu3fvqqOcuB6eQxdyhqZ1MWPxUiBj\nAeM5O5C2HbZnOYPpJELrmS+1r8bJZHCAB5jA0Tuui2gr+ayAWs4UhGkZXn5ev34dq9WqepZrz8mG\ncZyJQbwBhs+2QuFEDGVFvJvSh8ViEW/fvi0G+de//nUsl8uyA1zXbUsFUWZ//OMfSwQM58ZlLvQ9\nC7ANoZkm08j3o8Tys9ygKdc74mTnreWQuC/8jfKYTqfx29/+Nv7whz9UBiJiOCiZSDQ0gl7eEIfA\nAWsK8iYGLm/F0cAZ8JEnjN0G1ruNtcZk2jnyZb7Oc+HrMr9noMn1OD/5ntZ8ZfDt8TE/lpN8DYaw\npYBafOX3ev5zWS9yZl2AXDrqyvwDzgBCNr52AijRxvBEDKAV/mRMrJVgnpF/ZJoyT8sS39uBsoMH\njZwhtxPacs7rSY+IrgYNGdgabKA/ccagD0bDAAjeoayf/t/c3BTQ4i3pI+q1dKxxAYBh9BgfDglz\n67VPGQAYHEVElXW2vsaxtR6xY2dDaJ2yjxdfkreW3HFdzq7ma3PL7/P9rXtziew+EG4d63Ga7ruy\nmEufhu8MiKz3mL+IKDzlstlcvrlerwswe/XqVQnc8GzrWbJLzDlg3YD2+Xlbykf53tPTU8zn81gs\nFrFcLqPrunKsRy5V53noLTus2clq2Un6bbvRAl+e61YAws8ma0s1Rt/3pVyWDAoYgKqJ+Xwe9/f3\nZZdd6MV8O/AFf41Go5Id5l3IvPkGns7rLM1DtgEuiQa0W8caP2S71QLq+bvWNbnRr324hL7Cj9CK\n7BTBy77vS8CBPt/e3pZqlrzpG/1cLpdld3H0pGXTc+3AJt9BM+Y6zwV/gw1bFR04OATZfRxURH1c\nB++dTqeF31iPiG7mGoL18/m8Ks1Fbh3gdVCXccxms7LBpW2m73W1mPUCv7M/hvUKNgzZpRqNPQKM\nPxkD57qyERj40ZjReJCE0OnpaZydnZWSYOhD8MvY2aXuyC82MeMd/t9sNrFYLOLdu3fFPyBQi00l\n6LZareL4+LhKsuBc8nycW+TPOsgOec5i2unM+M7JkxY23Ne+KifTExARlWIDxHGtFT3XWnCz0m8B\nDTtDVpaASwSSSYPoRPkPDw+rCKIBmMeVo8cGGSi80WgU0+k03r17F/P5PP7qr/4q/vzP/zx+//vf\nx1/+5V/G3d1dTCaTOD4+LtEhFAQL2gGzzpAwDt5t2tpgtBwNM4/nwXPg52ZHPytTWsvpdX8dvSOb\n1HXb0tm//du/LWP1DpS8h6gU9x8eHpayRpcy+ywig0tAk7MZBlARURllSjSzwuf3DDDys31NpkkL\n5OTILp8xBpeiGpS3+NJ9zQa9JUu58Vkrc+1+7wNqubwL+udy8hxs4lqXkQNSUaDINNd7LQUAmYCM\n14ShrJfLZSmdwYlyBs/l8byD5+EoGeD2/bCGmLETbXZklWeZR8qcRB9dtEs9W820zs/Nc+fAHgEy\nZAYdg1OJw4AMjkajOD8/j0+fPpWyV+tMgx76kcuNkHlnnwxmKEMy71qecKRcPma+/CWt5TRU9G86\naUMJtsH2S83PtaNoeWv17aW/3f+I3fXx6EjLRX6O+cU//t7jzTaYawi2PDw8xHw+L9ktABcy6IAe\nTqFpDcBBlngnYPXh4SF+/vnnsqsm4NmlpwBMdIArHKhiQefgdOZABC2X6RkQvzQvDjgy35Z35gXd\nDUjGuaOk1xkggjY3NzdVqRwl5zgNjBXbNxpts7SU1W0221I7r502nzNX4/G4HBhPMyBlvS3vyzbO\ntquFQ3Iz4G1953tbzqivdSbGoPnm5qZaZxoxrBHks4eHh1gsFiULyTuyY0T5KjzGHNsZhHbwMc/M\nwXme68qcvu+Lk5dpxP2WCwd+M93yGvjFYlGy29AVHuu6ruDcq6urmE6nhS+dODFtGZ9Lbl31gLPI\n/9CkNafmH+bSQUvzoftsPO5KwqOjo8q+MG/s0oqddv+hKfbHx+aBiyPqJXx2Ntkp1sksroFu8M3H\njx/jm2++KTvPwrvYNqqM1ut1TCaTYvfQy2AYfs863I6jZSI7mq0sJjwJRmjxWKt9NU4mTBsRO4DS\nQsrkZgVmgmZwYKWR3wmxuIcSET8LhYJgEGVgwsyMzhTQDKqInFgRsS6CCMv19XUcHBzEDz/8EL/5\nzW/iz/7sz+Lnn38u5RqPj49xcXER19fX1SHDFq5cwopwmz7ZCc20ycJOf1uRWZr/NmPCvHm+TLMM\nrK0s+34bYXz37l1cXl7Gzc1NBfBRAMfHx0XQ2FXQxnu9Xpcda2ezWVmz4iiTQQRRejsyEVEiy/BO\nBmMtemaaZfCRQSY0s4PfcgINjB04yUa11fYa+dhdl2aFihzyP8+iDxmw5OARzY5PywHHSDBGFChr\nuTIvu4wVQMVzeYadWIDGyclJ9H1fHKiDg4OykQWRfxtR3u0MDD82no5G25HODp83E9g3P6aN6Q2d\nPUfwE1laOx12wkw/5hMeRxa8ppUSWnQZ/SC7hF7kWQbvZFsAUi6rpN9EirtuW9aMcfUOtRkEm47I\nsKsKWvzpewyCW05X1oHmXXgOW8Bn+wCy9Srg66VW6dPtB9V3flZ2MvN3ft4+YNByLv0sy33mNdO2\n7/tSgjYajYrDg73EJjBffh4lndkh5loyDBwN5sh8lhFK9AgmUWbo+QecexyUC+K0WVdHDGXOBo2R\n9gbONM384KA3gSmyGEdHRzGdTmOxWBSwSh8ZD8CXQNaPP/4Y4/F2fZfP5nVJLhlK5HEymcRkMilY\ngjmmRDKf5Yg+Na35nf4RuH2J3wop+oazGPsdxn12jLHm760v6Ofj42NcXV2VY2HAeBHDciPGwnEi\neZ8N+mNdt1qt4uLiolqHjl4gUOasJM+g5NZBtByg47goVyqZh7F33nzSOIb/wUUE48Gyxsj0m+Uh\n8Bf2jefirPJsr8Wkb2w8BT0fHx9LsKJVRZDLti0r1i/Ml4N0/A4f+HfkC3khYOoNNZlfH8vlig0q\npqAN/aX6wAFR5AzbdX5+Hjc3N8W2GYOg57B1Hz9+jPfv35dAkHXOw8ND2WF2sVjE6elp6as3NTo4\nOChl2/ahHAgy7fzdvuwmjSCSsfFL7atxMulwZqyIdlaSZuX9JY+6BUK4//Xr11UaPDsaKNz1el0O\nu8VYMskQ3A4eE+qIAM/nGQBMwOk/+Af/IDabbfr8L/7iL+LXv/51/OpXv4pvv/22AKb1el0O7EWp\ne0w0l+FlxZQNn8Fni55WbozP49lnBOw0RAzZiqenp9j0fdn9DHpEDJFCxgR4PzjYbrjy4cOHUg4C\nvVmUTlQVmrOTIQ4+/ceYe6c5g3Xud4AhR308LhsPG7XPHzTO9qpLr1AoFbCUE+f35j5sNpuSLUAB\nO7OSFYEdU89ta+5Q0ICv/F6DrQxSM5A3fbjHPy41dUaWzwCsVADA35T5ADZzpDSPifl1KTSg0kbe\nGZLs1D4+PsZyuSy0Nq9ZuedMeN/3VWYGY+u+7QNyLZnkO8+p5Rgj737Y0GXDzPcRA+jCEcYYu9qD\n549Go7i8vKyynvQDR95ZfwOAVuk6a5To12QyqTb/Yux2Vs1nPMsO9b4f5gWeMA1y81zmSO8vceIY\nu8F4xG5gZ0eXJh5qOcUOZlTAXbZOvYltd+rdPvO7/Tv9zoEv8wgZNj7zMUMEHLz2mX6zUY9BX8uu\nffr0qSyRgP7mhel0Gt9++2386le/iouLizKvrOlHzzij4uyinTIcDuhgXdx1XXGItn/X05Tnr2Uv\nrAehH8eP4RBHRAH6jJXyWr5DxtBByI7vswPioDelfYeHh2Xe2AmfOXJACccbepj24CAH2Vs0EAuW\n51ay0Pex2fQRsZsp2Wy2m2S1gm6t95nvX79+HfP5PK6uroquR+bRE/T95uam8CR04P3mB/6mqgla\n2/6CbXDw4Rloy1w4S839l5eXsVwuK6eWsYBd6Iv1Yab1dDotWf7ValV4nffZkcZBRD96rwtoYIcX\nGjjI5mAl/GSZy46j7X/G34zRuj7Pr22nnX87xegeqgHv7+/j+vq6lKK6Co7MoQNKrIV08N/zTMkt\na8oJVsE/BKyfn4edn13Fh/M6n8/j7du35Xr7Lvf399VO3vAAeIWAbA70GBPnwGP+scMOvT0PfPeS\nrNO+GifThM5Rdj7bBw4i2tHClpOaf/p+KGOzEkERs+0yf9vAwrQQHCbnWiKNCDHvNChjEnGgiHy8\nf/++KJ6//uu/jp9//jnevHlTjkcYj7c7vi0Wi+J4bTabEpW14LYA/76sif+PqIXYpQ1m4DxPGRRA\nr+zAdl0XXb+7RhM6OjJmxYdjYGOKo3F1dVVK/Sg7wiFwKRTGl0OkEUoLEuCX8ogscHmsBrWZpnCl\no3C8x+DDEXOUt2lpubBScOQtK4kdw653ey78TPMC/Gpg6+tNt5ZM7pNbyzS/e+0iz/RusURgTRue\nZQNlB8/rNZEVA0v3te/7KnMHaOIadrJdLpdljehoNCplRN50yhlP1nLyDitwO9Y2uub7rLNagArH\nPPMexsCZ93wN9yOrzAXGFN0E/ebzeTH4lNMxVzbQDrDlZwNyGHOWP44UYFkA7858y7vNS63nmc/M\nh/7d4Mz6E1Bu/QVdc2ajFQwwva1LKue09wZh+x1cBwP4jHkm2+Rr+c7Bz21ftu/JXcXZNZ1bgQEH\nLaBV328zAZRx5jNUibBTRtb3fdk1lr45IOKgBuuvAfBZv6Gvj4+P4/z8vKy7dzmoq6VGo2EdF/fb\noTDNcnN2oKbc0AYdOXxnGc4BAmjIeYmUy/l9rNMiQ2tHk7Oju26boWHHVNYRGl9ZXnEwCARFDLut\n56ACz2e3T/cfLAWds+2uaROxDW60HfFWkMN82KXvfkl7/fp1Ka1erValisVLKSit5ngQMr626Tgc\nEVHxqW0vPA4NjQtdqUfFDPNlnLPZbErQjvfCN1TPYBPNI8xXRBRMSMnrfD4vOhPd5fWzdp7suMJH\nDgZmTOK+kNHzkRvoAT4HV5j3sx9gnQ0PZFvSsgcuW3bw/ujoqDpqhXfd3NzE+fl5kT/TYjablT1a\n/Gz+N+9nO2tb/qSgtk8mwGaYXx4eHuLy8rIcw4Scog8eHh6Ko8mRhdYrtt/GkthiJ6aQI8+5ryUw\n4uscJDWebbWvwsm0YYkYtj2GoQweTMSWwnYkJYMyG5MsiIAjCMfaR2/Lv16vixBg8FwKgCHFQMAY\n7gfMhHAhxI6qnJ+fx9u3b+Pt27exXC7LpgY//PBDAbS/+93v4ttvv42ffvqp9JfFyd7dLTtNpgOt\nBbq4x0Kc7/Ezs2PSeoejns5CGXQaqDkjaZDJs8/Pz2M+n8dyuSzrJgD2RKwsOI6cO4K9XC7LsRNW\nWERyMSQoH48RnsmAITtiLcho3oiuK9dk2rlP3MfngCyX4mQnMzsW3JuzP1lOIoZyDo/NY3bZd6aN\nfzLwdhDEfGnZR56YOxwPOybIa+Yhxtd1QwaOiOrp6WlRjvAwtPAuljyXY4wI+rCxg50ogkmTyaRU\nPXhDH8ac54Bx4Mi9NPcZeFmf5eCAeYwxwt+tchhkzuVc6Cpfi6ElSut5ICNiwIV+9HPRqRFDdDnz\nHUbx5uambLDG58x/5iVADtcYRNgxM9/5fdCr5bh4Llr05fsvgV70ILoq3zuMaxdk73s+tAOM5mBe\nRA2MeVZLZrPH2ZoT64scxLq/vy9lfY6Ym4Y4eN5N2JkPAByYgBnKffwAACAASURBVIwSlQpcg0Ng\nB9brrHFMl8tlteQF3X5yclKAOvKPXaDPWfdtxzD8nedje01x0/1Nmddsb+FFdAoyiqMDHeAdnELW\nxt3f3xfnGyfQzgy/A+yt+5EbZJkNlexQuq/eSI+jnJhXmstmneUxL+XSbzcHQfq+5lXrs8yb+xqO\nFJgtYutUrFar4jy5AoD1rLyXdWjmd2QMOcsBUAefsm5F37kEmrOG+74vTgWOsHUvuAhHGHlqYS87\nhA7In5ycxGKxKNcgm8gscm5s4U13HIw0zVxyjBNMf+kL70LebbPd3334wEf1GF+ZPsiws/fwunmJ\neZ1Op7FcLsvu13xuDOKdmuFxn3cKRgAjmp/QJ9zrgJAD6Hnsfb/dUIpEh9exg1353/fTclIoO4cZ\nT5i/baMoqZ5OpxXWyI7qvvZVOJmj0Si+++67mM/nxfkzOLYyz1GL7BjRDERaCh0lwW5tVhYG3Ahm\nRFSGEyBDxsxRWp+R2ALtm82mvJNncP/V1VX86le/qgTw5OQkzs7O4o9//GNlsG9vb4vB9nizAja4\nyhGgfcA1Ox7+3468HRMD1hYo5n9/boDta1BC3i4cBvfmDQbqEcMhwVY60MjvtyIhskVEyBE2SlhQ\nFKafnZM8z26tz1FghYYxQBKebSNVGei+jiZBO/8A1LiuFZHP8zcajYpl52/zUuYbK859Lcuof8+G\nxH1Eabokx+u6mKeIqEqW6C+fMf+srSTLzUJ/G1M7RWxQQl8oTcfY0D9ob0OPEfGxRtY5dqhtuD0P\nrUxJyzHgfztR2ejmSLAzWnbKeD4AF7AJMKNPGDeXElEKxDMBqcjOdDotUXvWlhMlxRAzX33fF2Pf\nddvgHgYOuWnxUIu/TG/rify3ZcLBGQcIHJQ0rR0EybzfkgXrSctylrM89y89GwDi0ikHAPNz9o0/\nGu/OffezTF/WSef1kcwDsnVwcFDOAbQz5zJ/grU4UNhUj90gEgeK8zJHo1G1yRB6/Pj4uMgxFUHM\nrwPA9MVyNNBltwLE/eKjlg+1D5MAWpE5yt4ihsAXFQPZ8c3A2NUDXdeVgBA7qXrXe3iFYA96zPrX\n9t5OS8T2aCeDTK6h397pvirjFB/2nzO92bZA/0w/xtoKEuWGbsYBxC7c3d3F9fV12QGUfuNgRkRV\nweQfy6lp8vS03eXfZ7vmQByfsyEh2IO17MzvcrmM1WpVaIqjnB1CZMDVRvAxMmTb1nVdWXOI/cNR\nQn8TMI0YjrNi/S6OI/NgBwY9TlAXGpF1J7vJ7/Cb7T99sc20nc320TjJut3BK+bLmUDe++rVqzg/\nP4/n5+dyhi98MBoNJ0QQUGGToK7rynpw9I43f2KMvNfj4TOWgJGMMm7m78fHx5jP53F+fl5wiKsn\n4V0H6DwfvKuF9f1j/vYSP3Q5y5PYQdk48/8XTmbXdXF9fV3+ZtJWq1UFHlrEys+xEfVnLZCB0nPU\nAaallDIiilKy4bExcmkc0QmAl9+PwsUwYlztkOIYjEbbM3seHh7KblPT6bRsY97324Xmp6enVdp6\nvV4XpbAPhDsaYWNj5cnfWaGahtnB5LtsJLIyztc5k4JC4f0AUwAMThW8gKBOp9OyVf6+yJj5IzuL\nEVGMPHOKYs7z6LktmbOIGIlHM40cOWqB4X1GtfUZwNZz2nLivYibMqh9z+U+nF0rKjuxpqEdGwP5\n/Hd2BOz87HMykQPG54OpkUOXMFqpusSHtRYYvs1mU9aIeQ2Kn0vWg3mj5JX+OSCC0ec58AbGtOUA\n5aCPs6+FH7ttZtsyk8sd3TLgpfH8lhPmZ0FDg26MjdeFQTPKYfOc4pTyPbowIsp8jMfjnV0SLQfO\nKgGi8npYj8c/BiotfvTvmS8Zcy7HtCxbjrOh9rzkSLHHCnhwZYfnonzWdZWngp6JpF+hUcupNB1s\n53Z5ZX8G07JlGTOA46B6B4Uc1AQE0yecwsxbm80mVqtV2fES58fn8FkOsN8HB9sNTc7Pz8v6Qvrh\nY8Y87/t0cIsGpm2mxb62xRLDWs3c8ly5uoYyzdlsVu2O681acBYMYLGD0AaHmvknYIQc8oOTi460\n/bQcGLiTueHc4GzfbAtNt1061A65eTs7nowDJ9z0az0fXcXuqfALwNm6nIzU8/Nz2VzFz2RMdhQc\nCB6NRqW6jfe6f8gfgbeu64rcoN9wXsikIjvOetpxNa2YR8brPStMQ3gp6yjrTQIcYKvpdFpVCTw/\nP1eb5ziY6kw4fI1NtI3PutROFhlLxrUPV0XUlRuMwzo946/RaFQFRN1/70rNHJ6fn5fSdM8RZf7e\npdr6HTnL2If+YCONOY2nzZOPj49xenpaki7wAcET+MR62djW9LYdy8FSZyctK33fx6dPn3YSP9Dz\npfZVOJlmMJSoQZC/j9g1gP7slyh+OxrL5bJkK3FoYGxvcwzwpC8PDw+lLAihYO0lDGTAmcsEYGob\nXNbT4Ew5SwXYZ2v4T58+lYgYistRqWwYrBgzIMrOJIrACt60pT+meX6mn2uBcVQK5cDvjj6Zhnbk\neS7jcSaXPjAPGWzlKC19wSnnXfxYie0Dq0XA+j46RapMgxadoWO+LgMbK4QWaPX7WkCXoIQX7fv7\nXDrJ5y1wXclk7GZ0zCfmu5Zz0wJ7eX4AVwZHNqTObPA7AI1NYpBrZ82QSQdnrKRdQsW5e6zlABiQ\nDaXf/DAel9PQ51Y5UJahMv8yiC0+sQzuRBK7rlmebdlrBT2sI6AFn5t+jMlrgQwgnHnhWrJW0+k0\nLi4uCrgiuwCfZkABX7lkBx3lzUgMKHBODW7985KTGjEEZxh3pr35vSUbDqjlxvcvlZnr4upd/ixi\nCKZ4/ltOb7alrZbNpq/L4M76AcBFOV525nI5njef4TtnvNfrdVxfX1frfDNdeDdAneDFyclJybYQ\nAHH2AsCNbfcaKN6TA4oEGXP28KVmvuXSX6L3oQmZuoODg7i4uIjValVtTsIcHB0dld3S0VvHx8dx\ndnZW8InHwnxR5QKdXLkDT9pGo4tzkD0iSjkvG5nlihmXmlZBkD18bhrZyYROzCFn8uZy3Nbc+CQA\nbHxEFBqhXwhko/9xtJjTiOGIE0C5MRe0BbfBd3akjK9wRtBjBFO9bhJbRQDMY7Vjh01crVaVbaSf\nvA/6WDej5xyEYlMhStDJ5FkWcHBYakZg2KXpzJez5OYvMLLpZMyYaWcHyviyZMjV4PnsO8BPk8mk\n8ABYyce2oNsWi0WcnJyUeaXSCT1kff5/M/duobZta37X18ecc615X2vttc5OTp2qSnlSqYJKAgkn\nMS9BC3zxoUQMPiQQRCOWAcUXQYkKEUMexNtLQChJCAGNGkJERNAIgZhQF8rIgfIYweMpcs6uc/Zl\nrbnmfd3m7D6M9Wvj1/+j9THn3mcru8FkjtFH76239rXv8v++9rXWjCus55KPkSvkgk2ocj0ndXH9\n6dOnk6C/g7AffPBBG+eUCzuVdiaTvtbvvpfx/tGPflQffvhhk2nLxlz5SjiZVdUiLxC/arqeMJVx\nD7RyLyXBk0GdFS4RhapplHQYVumSRM3Oz89bpNACDBPYsCFU3OfoiiO5BuW8D2FlJ7eqpbL89NNP\nJzuDGXzbSbODipDZuXLbU6En+Ey6Jt3950i2GdhjYYVMf53iaianb1635ZlMCwHrSeAl1ql4LSX1\nY5AB/qyBcEplGud0ogxiUZaZosHMwzAMk5CtI5Lwo8GpgU+23QoMUORx4Vl4+/j4eLK5hovrpF2m\nu6OERNzGcZnu5HvNC44EpqxCR88ipSNKyh+0ACSg/KGd32+Dy4HudnrMl3YqkVucU4w5AI36kUn6\nB+9l6o9nAp3ybueTcfY1ZDzHMg1TOk3vb2w8No6r2WjLrPWq5ZL2wotOacvAD3JikEB6EO8GnEBj\nxggZ9EYl7HZIUI3otQMUXlt9eXlZT548mchk8l7qGesbP9e7n/rQR4xV8nAPAKGHEmT35M0Gft7p\nW88q8fusB1JnZr/zPQnEm27qtCH1j+uCL0hnzb7gKCFHnpWEBuiK6+vrBpBJJURWF4vlOZrOJlos\nVqls0J0NTuDBcRzbsQG8H95idtOphqlPCVK5v3Nj0gOx+XvPhia2oR3IK3JCmh6ycHBwMAGLpO5x\nnrb3ZUBnQjscKOMiy0UGbanDO22jT8dxeX7js2fPJngnZWQNr4kOPQnI5ynWTRwx5dnI5G3kDcfL\n5yuSTkqd6B87HNbpdoqQM/c3g7ZONfb4Y0dxSOC7d+/e1enp6SQFnPRub0aHjq5a7SxMwM5rZj27\n1qOtgzipc32+uG0Y9nUYhjo5OWlYy1k7DlTgpMKTXpvpyRYHIRKrJtbyPSlr1pv0i88OIsMTw7B0\nNtH119fXbXkG+ok0c+8W66yCdP6t9+x026m0fMFL+ASsw4VXGac3b97U5eVlPXr0qPWRPzASfYQv\nKPnZTrnbgE5JRxSbcnZ2Vu/evatnz561I8nmbBjlTidzGIa/UlW/VFWfjOP4B95f+4+q6p+pqjdV\n9d2q+pfGcXw5DMPPVNX/WVX/1/vHf20cxz971zsYiJ6DA6NQLLRpBLme93LdzgIKg8EAiHqXMRQA\ni26J2ngGxQDDg+FIKX2kPpSYIwH8MXtCVILDgMdxrBcvXrRt2L02zKDZ9DJDJx14tgeYPC4JcKum\nR2PYYeA+gx/XS7TH9M6dd3sBhp7gWHFUrdK3dnamh9/akaMuR7JR4AQBtra2mgFOfnT/zHuMgUGT\naWklONZKEZlG6QBYQbq4TXYCLEMUlOj29nadnp5WFiu5HHP+rACr1pVVGgfTyWPqP/evd4/BvQ2s\nQSp0pw1EXW2g4DO/y7NUyIU3mPIYQJtc4wRd7AQxk3J8fFzHx8fNQJNK47F2n52ObT4xbeaAWhuv\n5cWJvJrvUraoPwMZBhsUDCYF0EIAA5rbCSfbwueK+ZgggxuyCLwOJOWmauX4XV1d1dHRUQ3D0NYd\nGXj1+pAym/zm99AXj3cvUuvxNNjs6b58zutY0FvQOvWteTE/+/3Qfu7dc6C/8Uq0s2db/Q5ogoNp\nHqiqNrPt/vnd8BWgnQAGQIkdVrmPtXPYX2bIcAK3t7fbOmh0FedtEjDCccU59RpHO3jDsDq31uO/\npMdQwzANnjJeSbOUx7li3sOGOJi2WCzq6dOn9fz586ZzoOHh4WHLfmKDo3EcJ1kUVTU52oCxMuZI\nHeFxYVwB59hs7sE5+uCDDyYbhVWtp0RCw5psbDXlsTm5oSBvBAQT1+VzyLJnYylcswPn9/TeC/2w\nAbbVloHk+9vb23Y8DXbFbb64uJicWYletA1zUBWcShaBeSj1nTEDzh/tr5oGhLiHZ52Fw7OegUVe\nkxZbW1vNHiKbWWcGyaFTb70mpYcjwHIOQpl3ueZlON7TpGp5BN6TJ0+a8+h0cY67A2ugi3iHaUl7\nM+Bn2U2cRbYWss+addqHjHI82PHx8WRsaY/plLzb+9v0uwP5tjU//OEP6+bmpn7X7/pddX193Tbl\nmyv3mcn8q1X1l6rqr+na366qPzeO47thGP7DqvpzVfVvv//tu+M4/qF71NtKGuuqKSNVRWpi57uv\nm7hmYEcSMFREJ5zGgyDBoGzJ7u2+E+w7PcKLn3l/DirGzdEfFDYLkInSXV9ft7S9vb29NaHppRbB\neL7mttgRsnLtAdoENUljjyNlLsrudFRHvBL08R6vhzOYQ5mijJi1NHCvmoJijDLHUJCW4igewuVF\n8XZckjZcm4um9uhkejsFyb/bIXB9BqTmoXQw6TP8wc6TvXYlMDUPYSyssO180b4eaOez67TR6EUu\ncWgYCyJ0fg/pWQaDTgcex3FidOkLTgzXiFTTLwyv07Krqr0jZ08PDg7q0aNH7ciEvb292t/fr8PD\nw+Y8Yag8fglO8nOv9ABD9s+Ojn9LvspxtoOYBpxARc5qpx4AyGEYd3d32/FAFxcXbSdHZo99zEZV\ntfQ9xok6kzc9rp41Nr8ZkJluSe+536pWGyjwN2e8oW/KxlwxEOLeBELwSc+pdEm9nDLt9i0v9h2f\nYVjNZA41r79cJ04as4TINPoC3rCzDF185i4zSZzRCHDLvhLoWiwW7TxreGB3d7f29/fbzsY4QT5C\njHTC3myb6Y7utzM21We0Z5UK2+qasYtz9tK/9RwB1jTf3t62o3yur68nG9IA1gHxrCm0zvamMlUr\nW4WTQL9xJB0Ascwzxjj22GDs7/X1dR0cHKwBU/dvScuqcVw6mstrU0ezx3umFdiGcfHSll4dYD3P\nxPr3lJU5mTINU2aNcyzf7rsDqPz3LCrZRuDHpCP6iXddX1+3YLoDs5Y5cI8dHDt3iS3AvnwndZhg\nKTsYUy8ZAYwDTj/ZPdAHPrKdNY2Ma9zHxK52zoxZGYucPU67SRvMx9hosgAePXpUJycntVisNkdC\n3xgbYiMYO+QH+iX2gRed3WE+pE7eW7XKkHRfsaXMKNN/b/5m3kk8lzaHdhnfpINJ20jN/fjjj2tv\nb68F/zaVO53McRz/7rCcofS1/1lff62q/vm76rnHe7qgyAPl+/JzPufPSWhHFqpWm4ywaQ/3EQVN\n0MnzCCVChIB6gwxHuLJ9DCTCibK5ubmp58+fN0GoqpY6C7i2EkwG8e5mCaKS5laEpmnSzA5e9ofn\nqvprPrPfBsF2CPkOfW08POvKd9flNDucz+Pj48nMF+1n10JmR+3cMAbMjCWw6vEnz3uGoucQJG+6\n7h6YSwM4jmNL3cCBccQs60ARs2Yni+s3L6SjYaPjcXbU0CA9o4w9EJ+zdznOrHtgFhNedlTVC9Cd\nwkqhfVa2dvo85rmeCLk9Pj5ukWR4h+8cNQTIAxiSGmTeps6qVWoq/OpgU+pA0zd5Z47HXHr1Wb5d\nl8eMazl+OOXQzjuE0i/o8+TJk9ra2moReutNeJf3IzfQEH2WfSaqvLe3N0ldmzoCw6QfCc56/Jq8\niKF1FDnlH3oxptBlbjzgs3Qq3Le0g5vqMrBNPeExbc/U1AFO2ZsrWSfBUACuadQDfqY11169etU2\n98FmMcOIk+hxgc+YnRyGoW1IQuaAj3FiZoA1v1tbW5Mjx5BFyyDj6PQ2j9N0LMYax758bSq98TSf\nws92FAGypOixg6l3oq2qRj+ccHQooN6zKd55nALd7YQ44APNvN7VjhYglHaZP9EbKwyyzq89e2ea\nWQap25kUaQMp7969m2zIuGlsjKOw57YrmSqbDhHPe5kOberhMupHPzqTzmPmgBqz1l7LmXoD/qGN\n7Mvg2dPU/xlEoL/wAc4U7+UM3KpqmMrrM3d2durRo0eTLD/r1gxi9OwSzqrH3em7yA39cgDbgS/o\n7v5a1ghyIlvQin7gPNIH9mgAnzCbx73YQ+5hHOmvsYGz7eAdsM7+/v4ksE05Pz9vacj0h+whNnlM\n+Uw5s6NvPrcM2On0ZNzFxUV9+umn9fTp00mwqle+jDWZf6aq/ht9/8eGYfjfq+qsqv69cRz/195D\nwzD8clX9ctVyRmDOSeR7D5DOOab8NldHGndHmBAIou88i2FFYO3UUK+FwUDHbbXCJMJIvU7Lc+Rp\nHMcWXeVeCw4RMKcd2Qig7BIobQIyXLMAmNZWCO5TjoMVXTq9po/pXDU92sTRJ4859PDZhm779vZ2\nPX78uG1LDqix0mXMLfQ26m6bnSfTw8rNyjRpY0WStPYY2PikMri8vKyLi4t6+vRpi2j3jBZRV2+r\nXVXPhmH4zaqqr33ta2tGPNsFjRNQAJYzNSXHNT8baFK3/xvYVVULuGRUzodQe6YTPrDRZFxYp+mg\nkNPOkm+tcA2giOQCCEhX83ElyKqd6p5M5HrNHlg1v6e8pk7rRS570eAcY/M1PGxnyJuRMN7eaS8d\nK+g2jmPb+p61Rrzr7du3NSwWtRPpQNDH5xemHuXdjKHbkjxo427a2MGERxxMdEomcmU+741TGu33\npcnc17/+9bVgXYIA80l+tvx5vOd0jp9DR7kvqzqdvjiv6y1XXufsPjD+aWM9hmwUhI5Cdh8+fNg2\n40Av836vNyMQ8ejRo9rf32+gzMtTmFUHvHMtbTW237MP2GRvZjWHHTxOa1wRdrZHC4+p28OY4nRU\nLQM6h4eHzY6x26czeaApQHcYVrvZY0MTgFu3o++Qa+hhPcVYIyf0iXag12yLTAfza/JyFsuJx6BX\nrza8aTJ3fHzcgg0Em+dKAnAHuiyrdv58Df3pflv/53mc3HN5edkmEexcZgD99va2ZdUZD1nvUBw4\nYWzcBtqc9pixMybCeUGeLGO0gXugm9OY6atnVdPR7enutHdco2+mkfWFgzXmD35jTKiHYMLe3l5L\nUz04OGjH9Hj8fT4065Shb9pz8KOX3lEXG90h3+ANy0bValNBdCLPv337ti4uLhruGIbp0iLzhp37\nOVw551zawUS+uPbJJ5/Uhx9+2I41mSs/lpM5DMO/W1Xvquq/fH/ph1X10+M4Ph+G4VtV9d8Nw/D7\nx3E8y2fHcfyVqvqVqqpnz56NPebSeybM6GsWEtW9dj9CyveM/hp0svgXQ4QwsbEMYJK/ngGyMNlY\n+t0+uJX8e7YDp6Ag6Zfby2dS1HCcUpkTObGA9erydX5LxZ+K3v2CzilQVsZOK06A1HuvFaWBIAWD\nDPPTBiJgGEJHOlNRMSaeVeL+uTRY8xf0s9Izv6VCzcCDad+jOXUzxqzLpZ0GLmmcSEF7r/A+G8fx\nj1RV/ezP/uxoJZNGfG6saYtnG3oOpiOKlusE/pYfDGGCH0eULXOMv3d2dnDi4cOHtbu722ZJSNOb\n0zHQAoVKv3FkDORQ/qTH4sAmCLPucgEsu//Zrgb/NR45Fj1+HIahbsexHYFiXWUglYC3N5NruTKI\nIkDDd2/WwuY+OH9HR0ctnc6AeWtrqxbD1OFz2dnZqb29vbYhB7KcKbMZxEAu7Fya7/Je86JBB46G\n9WjyCrRCxzg6//6+JnO/8Au/0GSuN3bmDf+WALcHEtq4S+fwm/WKwe2qH57dXKUuWj+5r+O4OqYG\nmloPp661AwVQpW8E/MyTlgN2e+ddrO9CnpmxxEZz1jFtwK47cGtAiK7wO613HCT1+KScp41YXVs6\n8D3dz3/zlXUhYA69xXqs7e3tOjs7q7dv39b+/n5zCmgvz21tLc/YxkFI8Ei/rLccVLEMpZ6nMCNK\nHQT+GIc522I6z/G++T4djU12siRzv/t3/+7x+vp6smnUpjIHxO0wZmYb7bO+oVgmqcc8Qsp5Bves\nJxhT1gpyzboZ2pi/CAJ5wz7Gl/byXmbsLAvDMEzOizaO5b04ltbFOF44UA6emH9s/023xNWmsfUh\n9zuDLH9Lveg+Z5u3t5ebFBFQxmkbhmFNh/DZGBK+QGflJAXOIe1gZt0nRNB/2gbPYAdZAkBWw+Xl\nZT19+rT1wcf8YctyEgL+Ng7/vE4mAY/vf//79eGHH24SqS/uZA7D8C/WckOgf2p8P6LjOL6uqtfv\nP/9vwzB8t6p+rqp+8x71db/3gK+vb3rWjhgExhj2nKft7eURJq9evWogFWGEYXIxspVvT6FaOfka\nAI0p+aqaKDQYisJAu1/pzNlRQqFU1YSx+S1TB0wjiiNO3Jv3892gIg2A77OyoR08k4rFgCMBUjqo\nHgvafXt729Jktra26tGjR80pwTAuFqutwRPMo3jsAPQcB3/3eE8inePY0NuEfjV1JLJY2cCHHM+R\n6bn03WnGFxcXk/F2SSOf8uRxML09i2kjYaDmPiZISeNCHwFWGFDqsbF0UIln4QFmzfb399dmLm1c\nc+zcNvOW70HJ5/mpgF34D3r1aJK0dervlK+GGsfbtqFPxRyJ9QllzdFoz67AR+q+BLd2NnvgyLON\nyAXgzbPqW1tbjU6kVLEZELrNa1h43saVcYZGjAuOvgG16Zc6IGWW+g06eoGQqvVdiedkxTqVdvVs\njIvbxnfq633u6eHkgbSDFGTK99g2mQ/GcaxhsaiSnJi2wzA0cJRA1Slrfgf09tpwjzf32kFK+rET\n+OHhYVt/+ObNmzo/P2/vJNhq3c3RAxcXF5Ndw9+8edM2YYFGyRPMxhgcO4iYfNV3NBnvfuYQ/fSS\nGdtZfqMvHLFxe3vbzldEJ1svsdbVabWs2aKPdsStIw0uffREzuqa/9lsiSAQa/mSt6HdNMjRm3lf\n8a6xFc+bx1Knu9zc3LQlDvdxMo3PDMYZA3jTATkK7eI54zTrBLedmcFhGNpY2tmyviRl3O+gHaaB\ngz8+jgs+MZ4ALxK8sOwwlgTsnXVAXT5GivGqWso3wVeeN5biWtV02cpc4C9x2JRXNm8WZdue9sHO\nIuNEcHpvb69evHjRdBjjhlNoHGteHobVqRR2FKGTsQ+p3M5YoC7PpPO+qhWeH8exyTn9I6Dk3aUt\nQxlE4ZqDpPzOdesDf66qev78eX3/+9+fF6j6gk7mMAz/dFX9W1X1T47jeKXrX6uqF+M43gzD8M2q\n+n1V9f/cs841xb0JoPl/fqakIa+qBoBsGHgXUQAAK5F5BMRCYDCSBsYD6ggoxVFxg18rNaIVHLVB\n5BjmRUABqzCY14elYeAd/j41iFMQhSA6LTKj+ansuZagyADezxkM5jhy3U4Hys9GCkeCd9oZcDTW\nbTBdvNmBaYCy9hqVpIHbD28xxhMHWX2aKEV9T/CYxpw6Dw8PJw5sAmjABrNHOKQu+T4b6wSX5lkM\nrteDzP31gH8P2CeQ431O34H+7gdtRkaOjo5aClk6vMlTbpsBgw0w8sK9XvOFkfT6JLe/53Sbpp5N\n6QHUpaM574i4jQak+S7aOkcPp6OnjuB3AyO/Fyebmanb29s26zKOq9QignZEX6um69Ghe2aIALJI\nncWh9bmKHrPkNY+J+c2Gu8ePPAPwd/piT59bB/r+LClrPXDN/2EY2vFHqR+SlzJY1Lt/ExDP34bq\n29OqmoAngyzzRcoYyxV4zuvO7KBzn1MGmV1gUx+PEfrIFVNZmwAAIABJREFUm204ZR6biDPGWXQE\nPiwXpj0YwDZ5NV5DQZqe7Ux5HQYczD7dE/iVxtSBNh+nMY5jHR0d1TiOLdXSKarMSlEH48T+BLaN\ntL03C9/DOnznXtpnB5X1unYeejpqjv/ff1u7nkExy+ScowEP4MDdp5ge6AAHQKCD70954Vnjhwzg\neykAS7Pok+npfQAyEJg8mjaV8Ux7DK1sw9DVt7e3tb+/PzkWA75DByff0lbahpOGjjZGclqpx9e/\ne5z5bJrT9zldmtiMkmm8yVOstSSozH4CyIxtDjYNR9y2lBl+O7QsF0k8gkySDeQAUDqp8JIxOZkD\nnFX76tWrOjo6ar9btxnnZiBlbjaT3/Na1VLffu9730sRmpT7HGHy16vqF2uZ5/6DqvrztdxN9mFV\n/e33BOOokn+iqv6DYRjeVtVtVf3ZcRxf3PUOiGHHIdpwVxtnf0uHgmupGL04l+MIAEyevUzAd1ef\nuNeC5IE3gGHw0mmykiOyBegiWun0pYwy09eqqfPTo1/S0oop6WrwaUbmOYMNA758n42GnTgriayP\nNmWU2Rs1oRgRVoApC/ANWrwxgYEHIMYGNR08j7cji/6dNjrvPoU/FWYqo6rVGZJ2NBLYQEcW6aeB\no6SR8LuyPjuZucagF328y/FMQE8b+Y/c4siQqmGe2N7erkePHtXBwUE7c7Fn/HsOo8cyx4gx5H3J\nb/CWjWXyd9bpz4vFYm0G0/JQ749JMHif/t5PtfY9rjf74WLZtqz4vY5+JyD3uh1nR1CPx45dnXv6\nl3Wu8CXGlrFbLJYbPTEbAxCg/U6BSr40aE4AnY4mPMd7STW3zJveqQ8A2QbhLo4aJ0hKJ7JCp/rd\nKZ/ZptQjBuOb7GVPN1M3sj8Mw2RDugyaWt7IDDJIoz3IOONJ1on3Pjg6OqrDw8MJgOM9Dx8+rOPj\n45ZJYDrgsDIDxGZp5jHr4gTDPdla0vW2xnE9m6An+6tnTdPNwQYHI9ET2HwcRo46ODw8rJubm7bz\nvIGtZ874I1hO2mXykdd0OpBke2pcgg3gXSnPBL1tk3o6qFeSNu3/zL2JafwbZ0jeB7PZ1nlGx2A7\n5atn65kxdkZcz0kGE7CW1XRmP4Vc+0wdOCOWS5YY8D6PkfWpseUwDC3LgCVbPpqIPubGmA72WL9W\n1SQw70KQqWcne4E+tz1tj/k3//dwox1bj4PrQ++8fv266R9nszijcHt7u9k39KuxEe9giR3rgj0T\nCU2GYWhYzXiTOixz0BbbS9osxx5ZtqEn+jdxpnk9ZyppX282k2e3t7fr/Px8o0zdZ3fZP9W5/Jdn\n7v2bVfU376pzrqyDrbX6Z41gTwgt/AmmzHSkccFgME8CDjOk/1NsBBG+Xnt6fc7ZBjsrzGrC3Aj1\n9vZ227Lc1zL6kVHnBN82mCnEPYd0zgnKP9/fM8A5xm4jCsvRPgu4HQpmN3jO66jYBj6dHUcG7Yzb\nwUWpGBC7LS5cM03naGV68Ww6nXMGET7ws6atn2P3Rxs4FzsFrV1Va7OuqWS8C+Gc49hzODcZkjlQ\nw+J60uy4n80/OCrEs0dz9EtgmE6AnYzeLJjrTKc5Zcht8e/I4lygZ3VtTl6Gquqvn+05NdDUqV6u\nz5FNt9+AgbG3TEID85YNEAVHk9QrjCl1OcjmWUCvM6+qSWYHINbtdJAoAY/5sRfwmAuIVFXboMGp\nxqnj7LiYRj0eTAOfvNqbmek5I3xOepsv4OGe7dpU1nlxZYeYjcR58Gyb9TzjySwMgYjLy8vJ+OBg\n0o8HDx7U0dHR5Pgfz7Aw/kT+vTke7UndTho9R4J5/JNu+Tnp0Pt9033vqeers3R3sMLtg8bWi1XL\nTVz29/fbRkDMXmCvoKnXZQFiq1Y7Q2fQwwVaWh86UIDOdLCSdhMw4H09GlF6vG4e1o1r/N2neTXa\nXVxc3LkDptuRMzkORpj+PZue13d2dtoME8X2xHS3bOE42MG0fk2MBV87+ON38ceYe4Ofd+/eNRzJ\ne9JxGsdxsv6SerGXVavADcERghrYn97mOD0sQLvpG+9IHl3+m7fJPV6z/TU24rs3kcQfwGlnljM3\n2Do4OGjp2KTn41BSp3d0ZmyQa9OD5w4ODprNAYcSZKqqNuvJeBEg8CZB0Ardm3xjXvfM/dyMZs/Z\nnKOzy5exu+yXUjYZPwO1BHX5fIK8BBJ2soZhlRJVtYqy8JfPQFSDlzTwtGXOocz7ew5YttHPGeDx\nBwMlgKXfGcFJsOy25++pOHMszMh2vHy/jYEdCvfTtPa4GfQuFouJkrJQWFmg9Lx+jt+IouXumdSf\nUage+Mi+Zr9S8NKJpB8oCAOz5Bu3oSfU0MYygTJzJDLbW1WNBpMxG1cpW1WrmRmUi1OR58C5Z4hT\n6Zt3bKjMnxgqnAzS2GkrjgsgM4Mq5tvUBb1ossePdvSMoMF6jnfvXck3pk2Ob49vJtC01dUmuPxj\nXFyl9FGvZ/Xd3pRj+u7fGBPPiMMXyBc6AD6hLzzHeWrs3oehyrW9BBZwJHHyuI81Prn+L4MbCVac\npXBXIMQ86RkZ82jKt3kO2sCXLnPBpATWjQf0u/8baPPdesK61O0y384VxtLFzlvO9DvYQP+YRUR2\noQmBBgMk2r+1tdWyEoZhaBtZsNmJ5Zd0THgBcIY+39nZaYEp0m0JHHsmwDRLvWV+8LinLdxUer/3\n7EvaAO5xcBO75XaO41j7+/v16aefts+2a8igxwt7WFWTIxaqam08qcugdRjmzye2jsZeJF6ao0nK\nQr7T7/H/TeXm5qbtZHzfYj72Zy+VcOGa7QfjZX1lHTcMw2QzJoJkVUu7fHl5OQmkpfNrHuIddlZ7\n9gnnAafNDmXVaufZtBUOAoGRjDPHcbWzO0EdMgGtY+1MWe/O4SxjuXUbux4EukunVU3XfxLsdBud\nBejd4r3ZHLORDjoga2RV7Ozs1OnpacukwMF2f6G3+Qon9Pb2tu1cb/mBxvARvMUGUhzr5gkpP9Oj\ni1Nl5xzMTX936cCvjJNpJVO1Dtj8f45gc/X6eYSFwSFSBEP1zrdzXb22Wnn6P/1wn7Jd2TcDPQPc\njMqbFt7ZDUYmXxxG7Alx/m1y1Gmb2zLnEGV/DYgQqF57ejMANqieYXIEHHowlihtNhWpmkb2iPax\nvX0qu3RoegAvgYed5XRU7VQn/dOImjY9+s/xkPnl4cOHLSXKPO9io9krXAdcMn5svGKjymd4zQYl\nwVo6pAatLtQ3juMkRfj2drWTaQJ/g/05ILJJ3npOMb95jBNo+nqCjOSD1Fs9MNDTGXOGdLFY1M0a\ngBqbz0m9GLlMm0nHmv/Jr5YRHASitwYD8FUGtXg/zkamPqeDRmDm8vKypazDr15DhHHvOZkeD4Om\n5MPec+ZLNltp4xc6zvrNYDBLAufeWN8FhvP+5LEeH7nvfPb4UHr2iP/endIyg072Oi1S77wpiDed\ngaZuH+mf+/v7NQxD01/Omsh1l9fX1w0Ynp2d1atXr9pZkQBBQK/tugGaZTJlKx3R5KdeSd0AndxX\neLc3tgaF/kx7kj+HYWjHmpyentbNzU1z0mkPwNO7orrvOIN2oOyoUAgSUXDaU8bsGCDrSaP83NOt\n+UzSHDlLfeUCYP48JeXUTu9c2+b+wBq0E370jFjVanbUDqZtN3xD+3BksIHGTh5D23H0MPJFXWnD\njaWcIYT80V7THZ4mUITjCx69i07wWjpUtNGbYq3oXoWzmbjJY+b356zr9vb2xN5goxi7g4ODNgak\npG5vb7dzQVMmeR+0sHzZgbPT6ZlR2k/mBk6u+cc8ypKRcRzbsUbMptInxscyZPrM/dkBNh/6nrlg\nqstXxsmcU9pZekKe9fSAHP/9u9MFSZEFtOSA9Oqbe1c6Aq5jzoFIxdsDeCgQfofpSCPD+OJg+hDZ\nOXrMtd2G0QJkpdLrUwp6vieBVEbVeoDd/cfpcaqBiwXBaREoE4wnYCSNKTS2UPfGf87YELzwfT0g\naMcvZwSqaiLIPcWQys10f/DgQVuPaYWbxUqE+v0elDv1sKU3yjnB+hxoz/+9z0lHxo41tLS3qiYz\nmCnT2X7zc46z5cntoQ1uT2/ck//n/nr19J5jiNoj47i2Binra7JW63pq+X15P/oNOXAGgIMiVVPA\nk/2wM+mzZm04kQHzLzoWHmcdmVPkb25WZ/5yhhjOwvHxcUsVN1igLQYptNdtTb5Lh7LHx9zjs/9s\nrNNZdGS3F9gxz+SzVeuznD1eSx4ojU3WCa/fJ9rc4ysX70rq+wBlyCZrIe1QAqxShxvgHh8ftzWG\nTjXzzCPtQP/guPJueIn1neM4TjYgwdZQDKqtf/nbJO8u40gywd0zmz0ae9yTN21LqmpyRBllf3+/\njo6O2lnQ2DfsLHJpOUlZQl5xfqpWs5GJl5j9dBos4+pxRg6cNYRyW+m7aSDatOk5V4kx6IPT2V3m\nAj53lV49OA5Z5uzHMKxmqxaL1U6vW1tbk4wO7uMMTJ91OAyrTZaMBQmi87vvydlXt7Gq2g6kmcFR\ntZ6yvbW12swwz0t2vYw9a597abGmj3Gg9RT8k8ED65OeXJp/uIf/Hn/bCfpnx5LrZNOw+eft7W3D\nVLzLWTg3NzctIwC5IE0W+tBndBa0pP/wl/n97OysZWEkTdCj8BjLih49ejQJKtpXmLNZiZnS2cz7\nHNS4y3f7yjiZmxSBlXdPifeMAh03ELUSc4oI6VxekJwM6zbkZ7fDJQ13Twiy3T3QnODI9EJAOGcT\nZUMkCSHKNs611QzuNvacyqzHCsXKbc5o+/ccfwsb/XWUK4EeCtizrP4NYQeYVK3SSh1pRRgBOD2g\nmuPZA2geM+pNgeQeH8ngtBeDN49fAth8l2cX/FwW7+I4N/NCuiAyw+xvOpE5c9m7Z44uaaA9QwnI\nxCB7nUfKlMcof0u+yff3In33AYw9Z+W+uop7pu3sBzBaffymeuAt837vWfQA/fVSAWQWWbezZlq5\nvQ52MC5sPMFGZFWrYMa7d+/aLPT5+XldXl62oIXfaeNG3y4uLiZrMpH37B+GmHa6XoMLB5H815uF\nB2CY9+bG1fX3Ss+u5PWenvA9E96MezNyn89Bk028nTzt4J/rZQxI9WMsKZ5FciqYU7l2d3fr6Oio\npUSTXkbxzCQzDgD1N2/e1MXFRctAqlptRGQQB88Z+PbAb9rZnr0yPZe/bZ6Vm4K6qnqfyu5708F0\nfT3djBwz1js7O3V8fNzsCJtV2X44rZXZEdu+cRwbSPU1O+KMN20nAAkP5GwT903A6EQ3zQPUnlPi\nYh5OZ9Ql7eDnKR7Dns01z2R7qlYz4Z7xIf3RckDGFbP/8Hs6NCwfcYDXDmEPE1kn4Qx5zPb39yd2\nA5nls7Pj6C+BDnSlU2VZw8iaaj/bwwTWsT2HpdenHGfLqOtwvZ7R4z73gT5hEwnC3NzctD0FLi8v\n6+zsrNkT1mqii8AoubcIPEDaP+OGruL90J5d2W9ubtoxTQSJc1IAfuF5B+jQ3Yyb9UZv8mKTo9n7\nrScXvfKVcTJ7zlX+lmXOQPNMVU2UopUeAr5YLNr6rh4I7Rl735MCkPemEzYnSL2+IhAIIp/9Xit8\n+mWGqpruKNszhGt9Wt7QBcs9Grmf9NGKwMDDdVrBWBhQgDCxZ11SwVgx2iFEiJ2y4HqJGtmBSaep\n12bXD1BP+qTyN2A3rTKdwvdDGyt909P97fGIAV6P5wz4UoHAM+O4OpdwHMdmBL0pVg+U38fZNC8Z\nFM8FM+zIOCqX4+M/y2ivPtMt+Tkdql7pgdQ5YJq0zyBK9oP71sBWp67NbeS+WuPrnjMDX2eUkjbb\n0ANa/B1DDcilLqe3Hx4e1pMnT+r09LROTk7q7OyspYcxFhhYAg5sJ88h77TX+r03u09/LLO02/X0\nHEzXwzEsPqIl6dP7u4sP0hHJ8Z99NupBV6ZOpv09G9DT73z3NUfF852MQdXq2Ab0Cu8EZDF7UrUM\ncO3v77fjmHCQrq6uWrCC91CnARwzpowpO9iiIwhQEHDjvnQMMuCVmQ1cN92hzGqc1se0N34rkk7b\ngFzljIszfLCRtu+85/b2tm36s7W11bIBHKyz45h2lt+HYWhODGthzVvpXIzj2MY0+cV953vqaT7f\npWNNW/9Zh+Y7/O4ftzAGxlS2W75m3qF9Do4gC4wnQbnPPvus3YeeNOZhrKB9ynXqIvrtM4Y920g9\nDx8+bA6tnZ6qVVq6N9ji3FXk2SmgVdV0JAFB7/xsO+xAscucXezZc/patRr/++hcy4B1BO2HDmQD\n4sC9fPmyXr582dZYesbf9Xnm2nrLM/rpWDurIHXI7e1y2QiOpgOJ6Ef0LGszX79+XXt7e4025p8e\nPZJ//G7+zzmnveCOy1fGyaTMGckEXj0AmYY6jYgNOsRGEO5SfCnI+e6MXqeh6QHduf702t4D5wmk\nLOwoICtfg+l0WtdoLwPm3zISZFokGPH73G8bV9qYht9KcY52ppfXlKTj5OcYdzvDPZCaKcZzToid\nHbcxFSH9tFG8vr6uYRgmUXfTytH/HNebm5u1rcDTWUoFnMVrkrnftGZ2gug4m214nHqg/C6n0teT\nLzLdmDbxXqfguGRd5jPzZq8tNjJuix2UTCntgaZ8f9UKlKah7LXFY+b++zN13XYAleu/vR0rXdLk\nE/jLfTLdLM+ph1L/cr93EzQAMhiBH0lh3NnZqfPz8xbEwMCbJwG0l5eXdXR0tCbPc+M+p4OSF82z\nvQDKzc3yqIj9/f217BgDcQBGjuWEJ2LsDE6QuxxP31tVNcbsg/nEdEvauN35W74jbS6fc3YN/ZUg\nzwDKR9xw9AiAySAXUAo9EsAOw2o9/jAMLYWPtYLpVDpYkemytNG6yPzkoEyjV9Bo+fl9Xfp9OvTD\n6h6liFZNbWHSbbFYbayTNtZjxRrpo6Oj2t7eblkCdlAmjrLAo2XCINQOkccR0G3bRH2M3YReancj\nVkcuxnFdD/b0ovkr7U5v1vcup2Ou+N1Zb+pyrmUQB34F5Du91fprHMeJY+mdXenngwcP2rFOpiu/\n8x7zs4P34zi22WvrdzKVkCkfn4FuxrGEF+0gQWPqhW+434F0vxu6mqb+HVn056S360DWeyV5vYft\nnTLLGNA3UmfZzOjFixcTh5/n2Qnd42kbijx7Ftu0yPNUjVWrlmvQ2VE6cRtBkMViUefn5219Or/T\nDnBt6vO0ZR5b43DfT513BXK+Uk5mjwHsKKSiTNDnevy/aiWIzqOGMQxK7mqbZ4d8vQfys61z7XX7\nqNPtSWDUezaL++T6zCjZZ99rBZfC2wM9bl8qoVQqPfCPwDqC16sjHQMUOO0EPNMu2u5r0Nqpsz3Q\n1+Mj+pEGBfq5jaapf2P7/Z/7uZ+rg4ODurm5qU8//bS+973vtUO3EwCnIPucQL/PzgHvT/BHIXUm\no4D8Z32Oz9Ai0m0la2OQhr93red44kimMXCEr1e3eSrrS7qk7Pj3HGPzk3/P+7KfWfqgdL0Ol3GJ\nuLrP9fSOP6/Gcd3pcBstNwkYeM7jZD4iOt5rB/V7NsnjSGaCQRR6+Orqqs7OziYHzNNW3kfKdkaf\ncToYO+s7Bxh6POS+ms9y1uD6+rouLi665zLagUaH9UrPEfP42oAnP4ktJvbImQ1zsr7Jdvr9qdN6\n0WsXdnYF5HgzmMVi0dZ/4Yzs7Ow0sAYf+DfzqYEafWO9J4CKtW1OwfXuqeZP08L6irrNN+i4OTp1\nLrYA0MqpXK2z1m3tWo8HobEdBIN++phjQX8AodbZ0AQng3RJywvOo+l9dHTU1rYyTl7fSR+8xuzg\n4KDLh42HxnW9lHTJkuDX/U/7exfgvW9JR83OvvWs22DeHYZhYsfJlIMW1MVZmDs7O3VxcTHBPlXV\nHDyeS5xJGy2njKednKrpEXEOzliPOjjIembbZfrpjcCgzc7OTtv0J48w8Z9pQOntG2Jd5OAd77OT\nk45Sz1Zm3YwzbaINBAMIeBP82t/fr4ODgxqGoc0s0kfWoSaWYSw9K7y1tdX0GGOBbJIFRBuZRbWj\neXBw0DaPShzPJMvV1VU9fvx4jTfdRtMmdbx5yjYmbU4P82T5SjmZLghLgqyuglcxc6YiNSEdWUoA\n2XNY/T2BZ757jtnpkxWwHbrsn9ueCrV3rwU4acA7nHLTA84w+qZ00R49hmEaobMCdD/9XoTR6VYG\nZwloEijQL54DpLCmB7BjYbdys0MGnTxLkfzn/vr5niOCYc/rNzc39cEHH9S3vvWt+vjjj+uTTz6p\nBw8e1De/+c366Z/+6fr1X//1Oj8/n7Qjx3uxWEzAln9fLFYpvHze5GQC0KxAoD0AEUPodb53pcva\n2PozbfV9vua+ZIp0T/7ztx6Y9Nh5/FPO5xzKiey9B5O9MblLNxkY9Ppi/VG1mu+Ykz/fn3LVu9fP\nOMLs9vHnGQnus/PjVFnLOPcYUKROxHDCu0TLz87OmuG+uLiY8LH76vR2Ayb308GxOYczedPf7WQ6\niHVyctKMPDTJdYbWCTn+bVxnjHwa+l6wsYYVIu/t3pn2J/ktgYFp20t99D2eFWAjMMbJjr/rdkB3\nd3e36RYCWPCC+fLi4mJN9nGQ+EzAwXzlDaOorxc0yPSx1EN5IHrPFvVktVfasFX7MKmzZ9et/3q8\nal3NmHhGmVli21TqBDQjhz7vGH62nFctbQW0TWcYEI3NsxOQwQnTzLTtOW1z9+Q188iXXYwZekG3\n1KuUYVitEccRMx4EO7AGOc9RHIah9vf3J45qVXXHxkEt61/e42DfMKyOPGFfEqeH8gzBZALL3uGU\nWc+qVfCea/yhj5El1kXnGCUNzS/mfWQB5xk9wG+pc3NcemPDs7wP/YU80AdSwllW9+TJk/r4449b\nmiqBGPqLDKBvoIknQbyBl3Ur78RRx0fxOvWXL1/Wo0eP2pjQfvqyu7s72V3bGDAxMe1O+vk383xv\nRvMuR/Mr42Sm8ukJbzptvo/PCXDt2Dky6On/bEe2qafg8r6sI2eT/D/TIueAYtVmBzMFiM8oDfef\nMgfubTAMInv1GGi48LtTDXq04h1OJ/GfDaqFwMYNOuaaMO7zdSsS0zNTfryGJXnIRp26cvzc1lyr\nQtv29vbqj/2xP1bf+c536vb2tp4/f17Pnz+v7373u/XzP//z9a1vfat+7dd+bXIuUxYM1KtXr9qG\nF6Yv/ekpExfqcIqZ+8A6JwDfXc5lD7Tn5+RhRyeTnglwzbNZj6+l05C02yTHKX9cdyoc1/I9rsuO\nBCizB+7vKvBpXrNcDjXUuAHmzvUx5cxgxE6OxyN3BeUeR5XdT0dOE0DwPjsgnGl4fn5ep6enk7Pi\nkFdm4J1CRdTZPLVJZ/bGLgMldkz4fHZ2VmdnZw2QGdxbl2V69aR0QDb/U594DPKZXmpdj397fDDH\nJyljabscbLCDWbVKv4dPmOFk3Rmb+7ABBk4mO6OP4zI17eLiol69etWyPIZhuekYaw29azhAD/7x\njFPaWR/5wF+CWcbZNrlHu9U4LcU7A08r2xfP3pW9OSxdUdtav592+3gY7CBrspCXxWIx2d3SQJUA\nJSDWM5jUBzBmhsuOAuOeKc0EJQ8ODia07Tll5iuX3rVe6QHhL6NYh+SMeO+dPV0yDKsdb72Om4KT\nZ/llEx4/byeCMYXnuU4bPMPkI0qgO/yOrJEqO47L81V3d3cnOt2p0FtbW5MNZeA1js2BTzzGDnLw\nOR11B4Jta6Cvv7v/5qs5G9wLejlgkIFUaOQxIQWWGcaDg4N69OjRZBMgH0UDTkJWXr16VcMwTM5n\nx0EmYAPOSvzDOHl/A367uLioDz74oMnqYrGYHLU1jsusj/39/TU+sd6jpJ9jfZ+z664vr/fKV8bJ\nrOpHHybArQOW5xwwGxiDaJirlwqTAjvnWGab/N+KJ9vt/mR6wFz9WeeccPEe+jfHSH5Hrw4Lvd9t\nx9FMxbNWxqZfCj1t4h4EKcFhttvKzb/ZoTRtnXpr8EB9KEWEFLDjwIN5p2fEsi8e55wxxqj84T/8\nh+ujjz6q7373u/Wn//Sfrr/39/5evXjxot6+fVv/8B/+w/qDf/AP1je/+c36rd/6rQm4TX5EOd3c\n3Ezo6nZtb283Y9NTBLe3y3WhzMrYyQckXl5etnVwAJAEavTPBsSG17JoHrNxgV8NHjPgkA5Mypgd\nha4sjVXDYn2jFSAit/bGelFTo5fOc+qlxRJdLt/lN0lWqadXzDsJ1NYc4Ph+H2fC9IQ3nALq77zD\nUeuqWtMTbosBRE+Hc5+XK+CMkBJ0cHBQ5+fn9fr16zZLZfDF5jHIQPbdYwVNPW6mhf+bj6xP0TWf\nfPJJHR0dtfu9y2bKUZbUa73PSUfL/PuLTSduMvBZx315wzq9B0QIomXGhNtiuqFT0Ve2lawnxB5e\nXl5WVdXh4WF7Bn3ALte7u7vtLEgALLrbMwa7u7ttFgY+Y5bBcpV8nHLA9Tn6brquvIdaKqDqOpqN\n7nGNtPPUFbaF8LXXhAHEc5aZLCVAKQ4LvJ1623oZXWEsxdjRZ1L12LnU7Z7DSj1e92/mQ+vB3qz5\nl1GsHx2gzntSj6Quod8eF/QothV+xYnB0fNupbY3yFM6bZ5BtI5FrhjLnOGqWh6Bw+kKzhTg/6tX\nr9oSAa6xwQz3w3+kjzq4SFvNez2bleOXthHHy89w3fS3Hjbd7AgaT1bV5Pxo6nW2zziObf+Ww8PD\n+trXvtacefpimfMac76znn8cx6aDvA7fvI1Nc4AZm0cdFxcXdXx83MbD+BZaJT43Dk7aGmPmn4OL\nKQtzepDylXIyiZj0mG5OMVES4KbDAiMCknuzAz1Q5998vdeOTW1Mh8vFQpJgYO4ZrvdmTF2Xr6Uz\nkvX06GWBM6MmHVAcFmyEh+e5F4WTgKMXneN+GzgLU1VNlC70mHPybZgQZJTrmzdvmkEwP2SEzbTL\nP9/jXPrFYlGHh4d1fHxc3/72tyepDii7t2/f1kcNqz2rAAAgAElEQVQffVRf//rXa3d3t0X8TR/T\nm+2qmVXhnZ6N7jkmpsn19fXkWZzv6+vrury8rJOTk8Y7BGcSjBuEU+xgeDx6joe/44z3imWaPgzD\ncpZxEY6B72u8Iicy5SPHNL+nbPl3t21ahhrez3K4jjknOEFTT9HnM8v+T8+7yrb1ivvlgFQaWssN\nAMlyZNmqWmUXeOxzrN1XB/0woACWg4ODevbsWZ2dnU34jI1/Xr9+3WZMcCBwRHKW1X02f6SM8zeX\nAr61tVUnJyf14sWLevr0aQMaBKmsqzbRftO1OSM+5/D1bFDv2U38YHrwGf2cTgCzVT0egKe2tpZn\nXw7D0FL7fe+rV6+aHUbvsD7s8ePHVVV1dXVVwzC0dZdV1YINFKe/ekYBp4k0Nzu8TgF1GqNp0KPj\n3BgtN9la0XApT8ug1VjjmqNpXZLBw5Q7+uj708a5XTgqb968qd3d3baOmPRHjyUOx97eXltrBtjF\nueWz0y+5j7YzttAUu4LD4XFP3d+jZw9/9H5PvJG0+KLF2CV1Yw/79fAaY+M1rObN58+fN/6uWmVq\nHR8f19XV1dp63JTBnGEy/uE+xtyBYwINjAtrPnPZjQMbDkBULdcFsmmhlyqgswnsJFawzU3apfxZ\n/5pvNulJ6OjnE+/5/fQJDMh3B1Fx+Fl3SXbFOC7T9U9PTyd6kTG/ublpOhJbdH193fqTARz0EsE7\nY1wH9U1Dgjm0if7i4HrdrOXV8pI2BLrMYYk5PLmpfKWczJ6iSIfDsxupeAxkU9hRDs6Fzt96Cs2z\nSL32ZR3ZF7cvlVTPoPTAAH322hvPMuQ7M4qeDJrOwKY2cw8g0IAtx8JrP2ij6Ypz6XSHvJf2u01+\nR8/JdD09R8cpDTzjrcQBufRhzjCmMPaUnh3dqmoKA/DEgcuc/5YFp29/f3+ilOinQS/A7PDwcE3w\nHbGaUwiAPdZdGDScn5/XyclJvXr1qs3aeA1Tgu+eU9H7nPxqZ9izz3Mp5eYvylbHcW2fh6GqAwyy\nfb0x7fXj/ZM1DJszD5bvWc8UsLNm8NjTAQYmVeszcT2a+LvHv+f0pNGyfuoBgRw3SgLf7I8L1z0r\n6pRU6mAN2Dgud81kDd/W1vKsOZ51WqqBEk6H22vA6OumZ/J0BkXQwz/4wQ/a8RtXV1cNuMG7c05m\nvh960Dbr0PV7q3BSElTOvceBW/PJXEl5cD20ifMwHdjAUQTcMoZOuXMbcoMY0soODw/r3bt3dXFx\nUcMwtDVr8AgbkZydndXNzc1kzdIwDO2MucVi0WYeAPqk01rvz9mQtNWmTW9s1u+ptXoTr7hM3lnL\nyNTqnmHiqHi21u/27A1HcwGaSZ3zekkKgZmqao6J1zM76wrd5fWDyJn7y6YyyHDql9RfSZPkw15B\nBnpZYT9usYM9V9I+pR5BD1mnLhbLGeTz8/OJ7MC7FOsd+kkKM/RI/Ve1mrEkFZrfvYkWs8xc4x3o\nCWMY2u2sKVKycX6wMwQVkDnrNuMG06QXwPf1uYDdOK5kbHVt3bZntocxRmJfb+jm/rM2dRiGRrd3\n7961jbHICGDWkj7aRrFJox1M40yKJwyqpjzuze3oB44meo9nyc7jfbYtxh7WY/5s+2JH1LP79y1f\nGSczhbRq6jjSwQTdCQwMYCzcXLcjSknAlMZjzlD4nrzP9yao8XM2FL0ZBdfVAwp2gmi/aYRj4He4\nvf6e4Keqf74Wz/gMJtPSYBhBImrq9Ao7sT1D7f4zxlzvGSne7TqhpQEFfXH7d3d3JxEs35vglDb6\n/SizBC8Jvi8vLyfRxiw44bmDX/IidV5cXNSTJ08mbaYNyU9ZxnGZUnF2dlYffPBB6/u7d+/q+fPn\n9fLly6Yw7Qj0vvfkMJ2NnjxRR0YRLfs9meoZ9J6cDjUPXvJaOpn5rr7jdH/QDn17eiD5KfucfJ7P\neNauJxtzfcqgkd+Z7XYk3rLL+zPTYZMDxDvtkBHN9do+z0adn59P9PtisWg7NRsUej1z711z/Njj\npeRr+ndyclKfffZZPX78uG1SBBDkb1PJce6NS4LyTc/yZ3vZ59d50J5ymzOVVTUJ1tnRhL7MXlRV\nc0DYwMIgd2dnpy4vL5sT+fr16+bcXFxcTKLxtAFbxhqm29vbyVon2k7Koc/z3dvbaw4WYLDHi/S7\nl+nU+97DAz2a81vPqbLdavXeZmBsPSDLUojkT/o8DEvgvLu7285hPD4+boF2LxfhCJhxHFsAlLTk\ng4ODtkkMDoQDxr3+jOPYUj75DR2VpQd0e389ehufzPH15y12/Ci98U+b0/ut59TD84zhYrFo65Wr\nlrOLqUucgmmnnvc508h6lNRY4xAC7KTo4hymMzwMQ9vtnlRY0tZpg3eQ9XmayDnvo+7eZlvQfC7g\nwL1THTkds9vbdT1p55xnCZAMw2rSBr6ENx0k80w5toid0Pf29urRo0f16aefTpa3XF5eNlpQL/dz\nrIjxmR188JQDpFU1meCxrue9jL8npNhUjXWaqT96vGsbzveeE/p5ZO0r5WRmxDiZsKegciYlHUzf\nZ2fL7zXD9YpB0zqzz81irDuNc/3Ofhr49foyBx4NXi1UyTB+ZwLNXr35vowA+/2AAf/mbdSrak14\ns98Yvk19tTA4DcDjSJuc4sezKDyDJdrWG6sUynRYe0bHETOuO7JLXzMIkLPUtDlpQVorqWf0EVp7\nG+3skx2Dly9fNiC4s7NTL168qM8++6ylI9qpzH56Bqp3PWezE7Qnr6A054Beyp/f3StdB3NYzm66\nDt+7CSS+/1TVWVjl59z2OYBqS5n3oDssnznLZrnl/hZced/Cofp6pKejsv0ZBU79YNrCH5al1NeW\nGbcdWnGNiDJ1sb4HcITDAth1qhDr8swbmfbp3+acyh4/p2x///vfr62trbq8vJyAQYDVHB/16Ofv\n84Breq03k+kgg8esp9uzZLAPfnKGQ+pHy7P1LGOQZ7UxhtfX1229LWvPqlb6NwEybcidw+FfvmNr\n9vb2ahxXO87y2ziOTQdjgwCetpO2+XP6YQ0Il2yheX/GnvC/FwxwIMfP2E6wlp4ZLmSEGUwcDjZ1\nIc3RdtapkvAdG4gwDsxi2XnMHUbhDfh/GIZJsMhBk01BQYqDtnMOqO/r0euLFmQrz+plDDweOT7+\nD295XWxVNX2BPDx8+HCyYzaOzOXlZeNLjqPx2ufEQMgf6Z3wVm5CyNgsFovm+HhDGvrvs2iRF8YY\nuvBe189zllEc4KrpfgNz4298MrFrgb3R71Wr83jhSz9vHeajR7yO1XJRtXSgX79+3QIs3ONjgvb2\n9uro6Kg57XYScfCgAwFRxpQxcrDGDvurV69aEAJdZFllhpPlC+ybMQyrNbqvX79us6ieBPGuxbbT\n0Ni+QuLPzytnXxkns2o665OKw9O7VswmUoJQX08wamBmpu/Vn4CsZ4w3ATC3ZdMAZbtdLIybhNP3\nbnrO7bzLmPWY0P1Kh7FqJeAGJRasnvGgLs9GzrXHz/CZiDWAz0I1B8ipE/569+5dW1/lcfF7kk4G\n6SgT18195+fn9fM///PNgI/jcvH26elp2wXM5yjZmaeNjnjT5qurqzo6OlozwAkesjA25+fnVVX1\n7Nmzti709PR0Qu+MtNnhsUExzyVQNz0MDHEOMo3ahtOl5wT0gPNdIL8nD72x7QP1PvBM2eyB/ZQp\n7k05y+DPXF9TR/pajx7Wcel4pUHp6Yie7vM167p0KN23nt6hD+Z1R5gBObYPTpnzzqMUR3jn7EDq\nL9uWud8vLi7qo48+ainupP6ifzbxn/uVEWrzP8UzsKZrzi737jFfzemCHB8H+XhP7mo5juMEUPqd\nduY8XtfX13V6etp2rGYWk12yOS7J9FkslumfV1dXE/2zv78/+Y4+A/Tu7u621GqnMdsJM0C2XDg4\n6HHo0RmajTVWje95p6rtMN0Dxj3a93CDt7XmvfAYYJH+UIePeYAXj4+P6+bmpm02g4OKg43+zQAl\n6Zn+ztExpEZWTfdssJNze7vaBT31innSPNVzOPJ312XZyKDLFym9dvR0Q89+WMd4wx/G5e3bt01f\n3Nzc1O7ubuNFv5+xdWCHmeEMyhFIcRaIgwK0wwEcrhMEYmkOfOOMqmEYml71usOeLmCNr3Wb092t\nhzfZjrQ7xlemNe/IgERvTOycO4CV+BN9Dw3ZQNH1sA4SRxL6+4xnywx0JHDAEqeq1ZEjBBqgLTox\nbQnvhT8YF6+1xU6yv4Vte/Jz7/ucHGbg+T7lTidzGIa/UlW/VFWfjOP4B95f+/er6l+pqk/f3/bv\njOP4P77/7c9V1b9cVTdV9W+M4/g/3achPaXjHOJ0YPIZvs+BNwO7VFp3EdzFYCOdzx64pL6uAdG7\nMvrfM2RzfXZ/TAc7ZJva4XuSJja+Fnzf7wBA1WqXrV67k37eXRaDl8qHMUy6mg/8LrfHs4nkqKNo\niR45TdbKtefMUHLsUZ7UTX9cx+npad3e3tZP/dRP1fe+97168uRJ/Yk/8SfqF3/xF+vq6qp+9Vd/\nteX8n52dTZxM05E6GV/WhPF+FKcPce4pBkcmf+d3fqf29vbq8vKyfvjDH7Y1celU5l/SZ1O2gP/s\n2DOuuR4ti52QL1p6cuk2zLV9U31z4DH1TU8/zbXNPJ/tsyyZ7wySV0aeOc31di1/v7/RSB3QKz0H\ns6cXeyA9acNMQqbAezarqibGdbFYtI287HgYZPR0UY9Hezyb5eTkpD2f6W29+1fjuZrIBozbFqRO\nnz672rgix3+ubOLhvIdAGW3x+h47XuhNxsRglHMaWb9J8O7ly5dtR0av4cRZYq2aZz6qVgDZOoK1\n5Og5gJY3M0EfO8DJc+ZVr6k17+aavJ5tX97PdznrVWtn3SatzVdpV29vb9vGQckHqXNxFthshA2A\n4MeDg4PJeX3YFsAqY+rUyNy8ZRxXZycDfHFOcHRYn2YnhPRK+pRZAdl3fzcwhh+Rj8xQ+DIczKQx\nY2Q95Xsoia2GYeXsMx7YavAIR5YgRziJVdVm287Ozprzn+8j84jxubm5aetrWb+Og5n6j+erVpll\n/Mcx4XkH6617uAc9nGelOoUXW5b22+Nn/TMN6q0HeW3rrJ/5Qy+m3Pk5r/Ofk0f30TqdINajR4/q\n7du3dX5+3sY09WQGV969e1eXl5d1eHjYggxuP+9JGUaHegxw3F+/fl3X19ctg4OZaQcFTK9ehlni\nCI9PTz7vW+4zk/lXq+ovVdVfi+v/2TiO/7EvDMPwC1X1J6vq91fVT1TV/zIMw8+N43jnSlFHFChm\nNJgGJp2bUTEhHQk1s1GHUyt7hoPPCe56zhO/e1YvlV4CLNfVU2j5XM8h7l2jzwmies/NCW5vdrLX\nDjsKOWtpp41rBlPDMKwZ+6QBPJGRGL+7at3ApBFmFoSdIAFSvSiRja+BYjpQKXyOqGEADMS2trbq\n29/+dv3xP/7H6+OPP66/8Tf+Rn3jG99o65COj4/rJ37iJ+rv//2/39piQ5pKAbqQMkuE2wDJii7H\nH8U1jmOdnZ3Vb//2b9fp6WnbOY6IpBVgLw3WSt7/14xzyAogEOOVsy7Ja3P82OMdO2h+J/VkSRnv\ntWFZ99Q5yLrze8+xyutZV7Yrx375fVFV0xnJHhBaLNgwpl8Yr5ytsZxyLR3ZXluzpA73e9x27vVv\n/rOhzl0yrSPYgIEUv7n6ejyVwZMenXo2gO93rcmEx1c0mNZl2uSY9hxMz/x/HqPf69ucjRvH1QyF\n9S+g07Mf3M8MJs4Idbx+/boFAagLEMs5mIyfdcIwLGcAcALtQDIrV7UKbrIGKe0KwM2AC0DmrA/P\nkvdmghvNJufT9oNUSeeeI5XjOqdDmRn1O9LmAJ5xbADIZPiM42qTJXbLfPz4cdskyemzVVP7/vbt\n27q6umqA9vXr1xPHY39/vx48eFCnp6ctTQ8aeoM6Y4vUez0dZvr06IXT9mWWObx3V6Ed2DfPrsNr\nZEpBU/4TZN/e3m6O3uHhYZ2dnbXZMNpDMGAYhnavnTkcvaqa6AnaxnuY7RqGobWN9dFeP8l7cYAI\n2iCHODO8KzGCr83Rdw5PmJ7c6+wF+kkq+DAsgx/GTmljTA9m5Xknjhv6gvEjvRbsSBDh+Pi4Tk9P\n6/z8vI2LN1WiztwQ7fLysjmmBAuQD2i6u7vblhicnJy0tlh/oX+Ra/pK0AEsmziyp/cpOTv8RWYw\nKXc6meM4/t1hGH7mnvX9s1X1X4/j+LqqvjcMw/9dVf94Vf3qXQ/SARRGpgr2DIavM5gUO5gUExRl\n3gM4aVgS2M8BQurpAb45YEafEvj0AN1dys7vMg3nwG1PwVspwGA9GvqzFxk79dgpEv4tZ/io305q\nKgf3K8fMZwelEBkYbW8vt3v2ui0iQxhlFCkG1Eo2QTHFAgjo8qJ3ytbWVn322Wf17W9/u37pl36p\nfv3Xf73+wT/4B7W9vV2/9/f+3vqZn/mZ+o3f+I06OzubbARgx9HKl/rZrQ4g4fQ0O55ZDIjHcawf\n/OAHrZ9Oj/VY5/hbDnszQ+mMurhug3KPca+eLAnc8lrv/dmPqZys5iDW+X31/Ka650rSsWf8cqzy\n3uW1ZTt7/ezRI9vm6w6KWYbsbPZksafPsq509NKh7Y3BHNjI1D3ziINFNsSbxnzucwYt87ceXatW\n8oTTMpc9kDOq1rUZ+c7//O7lAHPO75wO7dHD390e1mZlsMvvJIuCttze3raZTXSsnWJsymKxaGue\nSDX2xjI4spydaUBpXejjTdC7OMZV6+n1ng2tWoHzdCbn5Lhd91TlzDNzWKE31ug56+RJ3cubNuoH\nb7Q3jqtslqOjo/rggw8a74zjKl3y8PCwOfZ+n2kHL+CEXFxc1NnZWb1+/XriSAKysV28j1kVOwpz\npedM+nsCYIPoL2s2s6eHTZceb5jHwDjIgVOYq6rNYLJ+j3F78OBBvXnzpi4vL5s87O7uthRWxsNB\nFuQC7OKMMNrlvsDvbByF7L569ao5nB5Trw/lfQSgvSEN77QjmFluxrI92zUX4OMa+iWD99DM611z\n9tclnSYmHnDceZ83CwNnou/Qa1XLs0aZpU7Hm36j9+j/mzdv6uTkpN69ezdJnwX/eGaa1GocafM7\nfMH93msEvvHOxW6HeblnE3OcNtF0rvw4azL/9WEY/oWq+s2q+jfHcTypqm9U1a/pnh+8v7ZWhmH4\n5ar65apqZzTZgFGyQyjSOSKp/rWBtVE0wM3SczA3GR1+Tyet16bs030B6dzvqYRzNinb6D4m3Xqz\nP9kW0wEGz/fD/E7DQGHaGekZdQPdNC7ZJq7NbXBjfnL7SM/ifYBXeAvFbyDDu1EyBmLUWzVNFU4w\nA/j9zne+Uy9evKg/+kf/aH3rW9+qm5vlAe9/5+/8nXrx4sVk98Msc87TxcVFPXv2bDI2OKGq69kw\nDL9ZVfXkyZMG/IiC5TvMF9AhgwM9p6DHNz3ADv39P41J1pMO6M3NzSolrfrytqk9vd+prX99VeZS\ngzeBHXiy58Qnr+czSTued1DMxjMB7Fxd6VRWTddap2zzXDpLqfvyvRVHv2zSqRncIIhkw82z6BkH\nFs2rfo/7f5cNmOPd3iwl9EbXaXalydzXvva1xhtDDTUO6zPROZtpec77MlKfs8+bHEzq3GSfAJ6s\nFUK3VU3PJgYgE1AkHZYA2Js3bxo4ok92WK6vryfrLJlR4X7qtY149+5d7e/vt++epayqVrd5lxlT\nvrNZGnVYB63JYNXqtEvbwjhuBLzh0uMv44XVs8vgkQOzHl/z8yaADg8+fPiw0dM6J1ORsYVOS6de\n94W1m/v7+/Xo0aP68MMP6+XLl/XixYsWCGAmmjWYXqf86tWrtnbN7TbPmT4JaHvgFhvmc6dLMvf/\nV0l96rXhPgMR+nunbAJSBFTgW2b/Ob+b4AtjhQNRtVryA22ZnfNsalW19MzT09M2KwcuYwYcPc/u\ns6zx9CQBvyMzPhuTgIUzDekPNFrSa2kPPMlgGbSc4UT17K2d96r1wIR1aNWU322Px3FsR/1QD9fB\nhdYTrNW8vb1tm2vRDvievnFUHBlu1PXu3fK4pqpqkwR2IqkP2dzb26vr6+t6+/bthOfp69XV1eR8\nVCZMcJ7hs/QPUqe4zAVA71u+qJP5n1fVX6il7v0LVfWfVNWf+TwVjOP4K1X1K1VVz549G61I3/++\nBkSrau1g0rv+es6WlXXVekQ3wc99nMIEb1zLunr15izGnPJ1XxKUu34blBQ2g8ieMTUoc9v9vcds\n2Za8bxiGSUpAr97sN4bWdHNfkr7ZXz5n6lXVUhkfHBzU9vZ2nZ+fT5zKXDfAM71Z6myvlRb9x6Dz\n/eHDh/XJJ5/U3/pbf6udZUUEamdnZzIbnACed3n8ME4XFxd1eHg44Y+dnZ22kUZVfTaO4x+pqvrJ\nn/zJkWi/D1bvFeQogf/cPT25y/4kwE/AS2E2Np+ZjMNsy+dnrHr33fXZdTTwRd3vf7ejsEmHmHfX\ngzvLVNjlpT54TWcJo29HII2sn+3JXNU0SGTjn4aa9liHmT/7umVJKctRyq/p05P5BE1ZUjYNVDbZ\nh3zXJtrdFSGPmagmcz/7sz87tmeHmqy1s670Nbfb1wF+Hs+kQYItAEavmCaMMTuLum2LxaIBVdKz\nTEc7nQBsZNgZGcxeQi+nEAKixnFsR5SM49hmFcgyQb+xEyMO5N7eXtNrAHTGBODtYhuzBHcr/m46\nR3SaEm45jpv0Stqs/M28x7vXr0+zAnr1cS8bkUB7B55cF/V591HkHiCaesYOEbtsPnnypH70ox/V\ny5cvW90HBweNtga77Fxb5Y2v+gC3h4HM0/z5WJ33/Wwyt1gsxs8Liudom3qih9f4DC/TT2MRnEOn\nZXomijrR5dznGTJkabFYtKAMR/egr536iqzc3NzUy5cv26wcQSHG1zIK/0BfAmdcI3Xd2QXjuErl\nNR/1adzHNdQF7ybNe9ihh0l7gWrqsW61rDMG3m3ajjjjQSDHATYcctL3SSm37kTnEbCBlufn521G\n02ueq6b6aRzHlo5L8Mg6l6Adjr4nUexn9Pi5x/tpi76ILH0hJ3Mcx4/VoP+iqv6H918/qqqf0q0/\n+f7avUoCAgsp/x0d8TNWir3fe7OTcxHHOWD5vu9dALLpmU315nc7gQkseT/9nVPMBvt+bg788b4E\nx+6njVX234o2mTGDAlW15gBvAsEJwLOvpm3P8faajR4tWT/EFuFE6A3ck5941goYxUVkNR1SR8Dg\nVQwRxtuG32OBckq68xvA8cWLF5OsgKpq/cuCscpZzLyH+jPt1bNFPYfSn1OB5fU5x6BFQ4fFrCdp\nOufY5/t6n7Nsuu6/9xeX0w+1dAVTX5mOKYtW+DmDQrXL/0NV9QJVU4DaS7FLo+LnDZJ6IMDy7Hfn\nrFLqttQZPefHjuy0T+s2wG3l/anLkscAAulAZt/9vvt85rlNARnkHHCdvzman8Cfz0417OlHO7Lm\nf/TWJnsz1/cMYI7jOJnFtKNi4IicVlVLFzPtF4tFHRwc1Icffli3t7dtF0U23WC25uLiov13gAM6\nLd7PPHrjmgcPHtTe3t5kt1P6CK19NIPBMG32eswV3e7HFz2l1FMf5s+e/U1d2NPz3GPAuVisH9dm\n0I9sQwuOOnDmTtVqhtJr+JyOCO0yg4zfDw4O6vf8nt9Tjx8/rk8++WRNNzBjxvsJhK7a3c/gSHpb\nZ/nPM21zWQaft7j+bEd+742pnT0Cydvb2w1npK0g3ZPPpj999B4STolcLBZt46fEIzin1IPjw7M5\nm0efmOW/urpq+hQnaGtrqzlP/FkncD8BCWOe3OfCNDC2yLHojQ10dmaZ6WMslbbSOjbxHceG4DSi\ns3HuuJ/0bzYtsyzu7Oy0FNxcmkcbvL9FVbVxYWLAbWImEj6inZZ76iXFmR1w4Ruv2e3R1jzf00mJ\n1+5bvpCTOQzD18dx/OH7r/9cVf3W+8//fVX9V8Mw/Ke13Pjn91XVb9y3XhvwXmQcJUjpOZMJTvjN\n9agfk3dv+u5neqDm/Y8NdLr+uXp6/U9nphd9cP83RWrSsbYB9nUrPP8GfS3A/q1qfSdEA5lMy8m+\nJi19LRX43OxQ7935Dj4nQOX6w4cP6/Hjx/XZZ5+1Bd3cY3A1937eC/1oL9HKXvvcduiYAYYU7myD\n6be1tVVnZ2d1cXExeae3Uc9CdLnnZNrwGDDOGVq/I9vc48O5NnF9koo6VPf+BDNJG5fl92m65lp7\nog7f050Biv7eV8+kbptr97Ke5WusG5b3re7JOrNMgGjoqKk+UBuGYTJ7Y17c5EQnn9uoOQUcfvKY\n9oF8X09ncAFZQ2Z7+t+R8d6f3zEnc6kPshjc5XiQMujjkfw+v9fButRp6Ja52f8eML+rZP+ZXaTd\n1k1VqzMWAakAJOQEELW3t1e7u7u1t7e3thEJzg99ffv2bcvIOD8/r/Pz8zo7O6uTk5MJzXGI2DQI\ngIfuxkl69+5dXV1dNfn1eqvUHbYzgLi+Dkn5npnxXhGzW5ffuank7w72otMYj+3t7RoWi8myAa8T\nzjF0nVXT7Cg7H1UrZxwnNmm4tbVVT58+rf39/To/P1/bQZix5s+pyu7nnOzZ/ue9dpa+TCczswvm\n2tbjJTs57p9nJD15cn5+3t7p3eLha5y6qtXaY+tlxtgOLHV7NtlZCsiexwL+WiwWbQNA0jLdv9S1\nngl0mrcdKWbXPXZJH2P6DGInJu6ND+2jzp6dMf8YX0P/ra2tlsHhNcRkCOC4Of0cfXR6etpwF5ly\nXjOO3rQed1+wY2wyVFUtOFFVbZZ0sVi0daAE4wkecQwUab8ELaBlBjpzBjiL6bvJ9s2V+xxh8ter\n6hdrmef+g6r681X1i8Mw/KFa6tLfrqp/9X1j/o9hGP7bqvpOVb2rqn9tvMfOsghJOpgGETmbGW3s\nAm//nkKSz1vxmlmTcf27lcs4rhbmU3I2y8RWD6YAACAASURBVO+6y3lI5ncfHbG2AnY90DTpaBpt\nmumgXivubIf7kL8RtcqSbZ4Dkx4zKz8DSNfVA7lJM89oZp9ZZ8IOXrzLSjdnDVBIjjoSAUPpe+Yn\nFWJPYHO8EwT3FCnl3bt3dXJyUk+ePKmqJcAAWGV/UWgYt7vakePna2kIelkDvXp67+R5z3a7zb02\nNBmMNq63oeOg6/1ukfl4GIb+FIX43zzVCwwt6bmuqzaVnv4Zx1VTso8rXltvbrs36lvnjeXMwlDr\nesH3u49zetLFDoiv9TIjXHoyksaSuhJ8buK5HIcemOnVeV+DrDWZrZCq9uzZs24/0+DP6QmvWXTg\nrNeOTboj9bV1JbsSQhvLZVW1lDhSIJ2aOQzLgDAgy0DT32mTHRpA6tXVVTtT8+XLl3VxcdH+s8bI\nOz+yMQfgu2q5Durk5GRyLnEveN0b56RPn36TeM0kMNN+6Oje5MNsxwb2mrQLWntHWPMKNghehE5c\nc/+w8R4f61uv/7OcmH/GcazDw8Pa29urk5OTNltNPaRuvn79ugFgl/s4c3bWeAbn6i7Z/KIl8duc\nPeQP+hnYM/v37t27tq6OmT54moJsgInHcZysx4THHUzu0c71eNMlzumEdlXVAjbgF1I2HZhhF1Qf\ns2JHy7unMj7M9MEzbEhlnWv5d1pq2hjTF/73Hhjctx6QWN9QK30N6nLbOZcX2VoslksFxnGVsuwg\nGTrp5cuXVbUKBngM+OwJiWEYWhABh5H056qpTJPaDu3YwZlAG7TLnb2dEp/6bap7+rOWPZm8b7nP\n7rJ/qnP5L2+4/y9W1V/8vA0xIXtRE0c17DBV9aMf/i2ZlHp7gC8Nc8/QpNOWDpzL3HcbimxPGpGs\nx5EwFMOccYSBrLDMWHlvHg/Acz0ANgcKPePcM9Jzz/qZbJsjZq7LSiLv93ups0dTfj88PGzrkKx4\niEABgng2gSGfifBDN0f2klfSgLnNc4KdhsQ8wy6zONVOI8/y+vXrds5fryQ/pRzlu3tgdpMsZbGS\nzfFJ2iW9qqpF8Hsy0wMHfibHxXpkQrkNgDGdinXZ78/KZbt6DoLHwDTp93E+MOZ3tN+H4b3D3O6Y\n1OFxMLhL+ZkD0wnO2/fFohbRtryv/VXVInjQDlavbT2edJ88zvk5n7V831WQu7x3HMd6/vx57e/v\n197e3mTdduor9yVBE2Ao+75Jv24CCMmDOJkJ2rEHTp9/+PBh7e7utueJ6HuDi94SF/OE28/f8fFx\nc5JI6T8/P6+Tk5P65JNP6vLysh4+fFjX19eTnTiHYWgzogDQTCNPWcrP6G3f3yFam6W8T+nxoem6\n/tv6zIuLnZdlfxY1jtNlHsl/3ItNcvqlMYSxlh1Z6MmsZuoe9PbOzk49fvy4Li4u6uLios2IERyx\n47mpj2kD5/SkZ/C+KBCeK/epL+05GIpdPXEQCYrBq1VVFxcXa5lEu7u7tbOz045hwi5CM6+3g7fh\nfTt5W1tbLT2WtcnIdqaIu+6qariHPqGzHDy3ziRbwdiH3+2MGqtBX+t33p2YJdcl9vS3xyxlqWcT\nqqbBYeM47mEW2ammi8XqLGbqoI2Mw8uXL2tvb6/NSJLGyoY9ON/U21vucn5+3lLLjT95H+s/GWfa\ngEPLbCYyZ2zlsU+69bDnfWY858qPs7vsl1ogoBVe1TRa0gNffj6NRs4uJGCbA55zpefIzTkJm+ry\nbwAlz97yLjvICYITEFetFEVP6Py732HnzKCNe81Y3pY/lRPXABY9hd9zdtwXC7ffkWOewMf3QivP\nIDpqxP1e00rd29vbdXR0VFU12Tqa+lCQvBc6uX7nzOdMrgFVtt91ZFs3Gdmk7/X19SRK6s2WXAC8\nm9Zjzu1wmyB9Tq56Y+P6U9lDy62trTYVB809g5119p2pocZx5djN3ZfBE19r/TVdpoRYczpNo+V7\nVk/12pHX3S7aOQXoq2esG9JRae9fa09n1rBdq9l7eBdyM9ElM3To6co1B+gOPdnq6NRHW+DTOd2S\nutO/ua5ch0adnhG6j6FNIOVyeXlZJycnBXDnXmjjSDog0sHBcVydW2lg3+Or7G/veu9ZgBWAxU4X\nKZPWKTzPrqLYgJw17IGX3pjlf97JsoZvfOMb7dimf/SP/lG9fPmybfzDmqnLy8sax7EdDZDvdbty\nxqQ3q70mP1U1Jk1n6JvF7fF4J13mdAXpeXbal8+s1vcZS1XVmt1OG1k1P0MJwPdnOykG3zznZRo4\ngex07lmblLWebPVsXdpHH7PzZZUcj0186+um6+HhYS0Wi7YWeRzH2tvba1lHzDLRTwdk9vf3J/SG\nDnb+bm9vJ8fHQQPWeHK/9Rfpr8Y1yG/ValkDWObg4KAODw9rGJa72MObyOX+/n6b5TRfeFbcWNYz\nsJShVphsju+Xz1SlPXXQqzcuidWM64yNbSvQy6ShejKH8QCrsO+F+ZpjfCwXzApXVXP80GuWLdrA\n2kx20Yav7NyaH1gaQF9ZU7+7u9uCTowH+qHH76mrTb8vIl9fGSezaroVPYo012HaKCQ4MwNTeszT\nA8M2JpsIOQeg83M+oy/uTFPS+X63dw2YiRb+3wOFXHdk0v3o1ZPOTlXfSbeD6rx+A6JsTwLOHp2S\nxj26JNjN9yR4yFmmrBvaPHjwoEWP8rgEnrGwJ7959tC599l32rDUmdPdzgw8s6QDZLpVLQHixcVF\nHRwctIPKUZTZzufPn280lr3z8bIkEOsBM3/eNPaTDUXUjh7AyPpNn14b83OvPz2Hs1uGYQUoQ4Zm\nHmhin/X33pltzBmgvO56UiYmDvLMGN6n9GjV6hvHiV6bk+v2++rGlUxU1RgzdnPAzrzkI3pStlLm\neiCa5/ibm8nENt01k8kzPScTuT45Oan9/f0ax3EC+HqZEb5O8UYOc21IOUwA4fvsVDkDwoAKQImD\naVotFqvjQ5b6cKjoenfs3CbPpHGPQd04jm0mhjY+evSojo6O6unTp/XRRx/VJ598Mkkbq6o1p92B\n2E0Bsp5ey/bXHYBrk6xZTjfZTBdw0YrOq/c4DXAuA6rXPu8/MMnekPwxDl73yHEM5qFVvYsahrGt\nXxvHse3gXrVygvN80h6Pmj6JUwx+AdH3yTT4PCXfex/c4mwwnG3PiO3v77f0WPrGmOL4Mc77+/vt\nO3iiN+uVAbCrq6s6PT2t4+PjltLOH/LjjIjd3d22ftBnne7t7S37MKwmgZhJffDgQR0dHdX+/n7T\nEbQbXqVPvbWy5nfrTWZ+CZL3StpQ3k1arMfPWJVC/3KGDl6mzZ5p5Hf7KHaMx3EZUOFIJgKX3mEb\nfQ/tCb4gh9TNbCfBguPj46paYU9mj4dhaIG1PIv11atXdXV1VUdHR2tr+N3fHnbp2aQvKltfGScT\nwUnD1XM4Eiz1AMPc9yypjNM5S8LmIFX1d5PMZ9qg6vpCm5uM+VvMPrldc8BrAh4Wi1kjiFDyfAJT\nvxsh590+k5JiYataj5q6zDFqj9nnxsTvdxpJGqakD38GGlaA/M5ZYBjvjLDBowiuaeKor/ubNGt0\n6Bgw3uF1JgmeM9ru/l5eXrb0JpSbHeaq1fEEm5wj0i7S4e0BsU0lAX+OC4a15yD6WX9WY1o9bl+2\ndVJoS62fCbfJ6b7rPb3C5eUuscvt+quWG/qYFsv6B90/jb5Wrc5Am9MvOUO+7OMwUSw9w97r4zhO\nU1TzfRTPwN+n7qrqOuhDVQ0zmQ49wGueAFik3pkD2j3noXd/glg2Vrhv6d27WCzq7Oysjo6O6smT\nJxPnLv+sU7y+3jus+j3WHz1Zy34mjatW6/iYGSDQS9DXusdgcsmj0G+dFhngNI08pr2g8JyDwczp\n4eFhPXnypH7wgx/U7/zO79T5+flERzsjw/JknrVeT1uUtidpOy/7/cBwL4ib97he6A2tU/+u2ZX3\num3OHvnZNWe7hjVaZ5uwi/y+Hvhc0ZM1bIwDaaOkkiadevoj+Tv1nEH7l+lk9vguf+/Jko95YX8G\nNl/xGj1Syz2rBO6tWul78AgOJ/Tzulrktmq1SdP+/n7jMc5j5BxOsAXj4nfe3t7W0dFRmz1lpm14\nH2ji2sHBQQtiD8PQnCicUMuJ5XhOnqCZ13xaLpfPrGhPwWm2HNsmQdccU+yGM2GS76ANqcs+Q5N6\nfazWzs5O7e3t1atXryaTZOg5+m18h751SjoF+by6umqzybTRe37QH+u6i4uLevjwYX344Yetn7zb\n9E+eTp7/cR3Nr4STyWDSQaabU6FWVYfp1kFkAnLu8/1ZZwKkdAqy3GVwUlnOvb+9Y5zOBvQM0JxS\ny3a3+mba69Iz/NlmR0fzPgMdfptz/ObeQ+kZf+7rKSQ/l1HhHv0NYvweR6oopIkgnL6X+rwDXA/g\nZyTefdpkwFIZG2QnOMkxQSE6daLnvNmBnCvjOLYUtGyT350Arcejvc9uW6aN08a5drW+ddrTK8Mw\nTORrqHld0HvfXWU1VpMn9Xv/uaSpZ108c2DetSwwA5g83zMgm/rYGmlwfUffbcDngnHZx036YEQH\ndsBtlnROPJNLvcl3vXqShxMwExlm9uXHLYvFoq2F3tvbazoUmfaMFKDZ+ge5zXSvdAg2ykGt60UK\n4LVqtWOpj6FyBH89JXbypqpad5ZSH2Y777NmiM+eHeBvb2+vfvSjH9Xp6WnLTOnNvFKHZSpxhcc7\n+aen73ttzpK6nGvQLH/H6eg5l67T715SfkrDHu/TX8YxAxy9d/g53zcXoNva2qrd3d1mRy8vLycz\ncZvsRtLN/3uBmP8vZjL9zvzcuw96OtPAjszW1lYL7uJUsIeCAzfUgyOKM2fHFaxctTp2BgcU58ab\nzDx+/LilUOauptCQmUzSfHH8vDs0esGyhRx5TbZtelXfPvNbb6ImeavJbYwFcpyzkinffKctFAe2\noKlln8/MUtqJzXR3dCQ+DTO86HNPaHi5x2Kx3MQH++BdY3nP5eVlPX78uDmo6GrrYdaAMsasA6X9\n6BLKnIwba+Zvn7d8JZzMqpUxZ8vzjIRU9VMFKWkkqqbRhgQTPGPjcJcTl98tAMOwmj28D6Dp3TPn\ndPi9Btjp4PSMcq+vdynK/NwzoGkYsu0wcs7EZZ94Zq6eqvXNmwwwe8C65wyZFyw8eS0d3dzggPpQ\nAOnwWeH1ihWmlWIqx6Rzj2b+bhB4e3tb5+fndXR0tJYmQsGB3FTGcbm24+rqarLt9ZxjljI2d1/S\nmPut0Axgeu+pDby4di/XYhymz01nERsNlpVMjgXIeqfjZQoO74H3lD/ctnXjOvm6Ud/RpwSUfk/K\ns2nvusZxnAWn7d73dGgdjTqq+gEf/+9nfShKPY6Tda+9qHRPT1vfTOteta8nx1ncj2FYbVbyedNl\n536j/y9fvqyjo6PmRNio28H056qarMekj5t0613g3fIH4AG85PpKz1p6HRTtWNa3/o5N9oyHho6M\nzRXrTAPvhw8f1v7+fh0dHdUPf/jDOj09rcVi0XU0k5d6es30ndOhWU/apOyv/9YxwHQsaftkGcF7\nXTUNYE2Pnkie6MlBzqrw2fzGdzuDAF7br7n1XVyzo+mZtR6tkoZuc8qI/wDwc/sIfJHisUr7zOfU\nTQbwOIM4FqQz4qg8fPiw6RYHbczTe3t7bQOZlEG3k3T1zDRzAIEMCOMOB5W4htxXrdZlkk6by4PM\nM7zPa7bhFWfOJX35bB7u2nz+d/RJyhRjlksp0KNOaWbJBfRIO+1Ze+/Su7Oz01KQ6aN1JU4/TjnZ\nY+4j7yKNmY2ivCu0s+kuLy/bWaVuW5ttfm+zrq+va3d3t20ARN8yRX1uTCzPcxkX9y1fCSfTg4tS\npVgR9xSPlaaZM0FqD/Am4FlX+tPrPZCYgBfwNQdo5pwErtkZoh/Zjk2gws/2+jznrFVVWx/YKz0H\n3wZtDoDThznauu/Zzrt+px82rnPP9mhkMJwg1WDThwrzGxF+zoejHQm2e5Gg5AE+p0Oe9/WMr++1\nLFxeXrYDmnsA6T6pReO4TM27urqqq6urevz48eRd/rwpWrnpu2mP0266zcqN2tirfw6sTuXe9y2d\nna5sdepwScdtCjynNSTI7c3uuA9zYDj7mqCn/b5Ypc33ZFQ4v13vtWccx+XN3Kg6fV/SIvVNrguA\nRsvbp45+8refoa/Je7keJ+k0R985mrJhx303/Xlfw+wvgMTLy8t6/vz5ZDOIXnTcABowwoYRvbb0\n+OQuh4MCEH316tUEVNJmp69tbW0teWtZ6fv6Ji1p75ij2ZyMxl2TurFZ0JJrBuUHBwd1cHBQH3/8\ncV1fX7ejVEyP5Eu+ZwBsrs13tX1K25VT2MMyqd9tX3rZXNRp2TBN7spOMe3of29mHH4Yx7G1h/sJ\ndACGPdud2U3DMLSxubm5aemaTvec0+FrWKtqMo6WSwIwX2bJGVP/JU0ZB+iFo0E/2ciF+pAvaOej\nMnDs2LE132F84SC5NwiiHv6gFQ4L9GOTQ96NM+wja+w80Wcwu9Po4V10sdcQpk5K2sK3PJe6Y45X\nCC54jSW84HRxv8POJAVHk+c82+txz+wHdDSZFKTL2qEnO5MJgNvb6fmklkHaOF3nvgr6v3z5ss1u\nUzwJwNh7Z+7MUOhhAfNyj9+/aJbAV8LJrFp2CsNmhd8D53zu/bnM/dYDbklAD35vNmhSbxrRoQ/+\ne06QjVzV+tmaBiCOJro9ptMceEoBSdpWZXy0b0jTsbQT1aOxHfn7MGmPPu6X+zn3mXQHP3uXs2ea\n0Gbq8MG4dmYxEKkgc+MMSu/dVljuhxXbXQY478P4sytaj+4otU0FQ/L69es6OzurR48etXSNHo/N\nXcvfPCY2XPzulJLs96bveS1/z/es97cze9/qypTjlJaesl46TSmbpl86mSn7Nho9/ddLV5/w+ijH\nkE6WnUMuD5PbUl56joJlPNOSenrKBF2/b51OPYNYw1Dj7frmJqZFr40OWhikJc/6z2upcqONXlnV\ns/G21rcXL160qLRTqtKwe5MOdn2dcxZ7tm2TTCTPegdMQBAg1XQepNdrGCZBn35/u2yokrwyTH7j\n/p4MVK305YMHD+qDDz6ovb29Oj4+rvPz8zYz5Gcyk2HNFg7DBFzn+/K5TTiF/vjeOfuMY4Dj0bOj\nveJ+Zb2NiuIr6x5slmekbiVjboeDsbe3t81BGcdxI62YlSVFnPf2MoWSdtYFvVkuLyv4MlLaXdLx\nzu95L3bNG/7gUMCjOF6sKa5a7VYK3R4+fNhmtaqmmUqmg52n5Gk7/blJjenHcUo4XvCBMYyf7QVG\n3T4HRuDnbFc6PL1xM6/me/jd+1aw3vfNmzf16tWryVE5tpPmoZ7z5EAIfTFteI76oQ1jwLjiqLMf\nhmf0SZnmeetW71IL7XBa7Tyim8Gpb968aWPjs9rZyRYe9NgnvXv0z7/PW74STibGgYhNKv05ByYJ\nlc8maJgzzHMGgu+O/PTe3wNUvUGca3evTdmu3kxi9jPvpV2OwCeAy2IHy/TMzz2Qx+eM+OfUfCrp\n3jj0nOC8N+nv+9PJ680MZ3usQJNOvf6h8BB+6nc0tcfLbgO/2Vj0jFr2rweUkg9QlD06zjmfPdq8\ne/eurq6u6uXLl/X06dN69OjRGpgxwHEbe7Li4mdsbObkpyfHvfdV8GZPrqtqcj7XJhnO5iy/9+R4\n6Xwu27nqY4/X5/jXfLC8NnUAl7fNZxxsNAQdGtIfX58zMD1eG8fxzhnTSZ+HoXuv35t63L+bRgY3\n9D9nwg1Q5miWIGgYhpYKlbOY9zG0PRpznXdcXV3Vp59+Wru7u5ODt03vTG3zrEOPfvctPRkCsB0c\nHExmV7zmp9Eq69OYqrf82vh3NR5rlNnQl871SRumY0vwb3d3tx49etTWoTmlr6c7TY/8fXl9dYzQ\nnD6ZsxnYi7TDzvJhFsnHk/R4FvnvtdkzW8t7+4FO61L4zI4mANf1cy/yBrDneQcj1lN8VyDbuIQ+\n9vs5H8RL+gOm5/r7RUra7Z4N7uFU6xqe2d3drYuLiybnwzC0tY9V03HmWIt0PFPXUY8dqdRjvt8B\nAs+cQks7f06zNc4xnzkFluu5Tpt6c0bRNLKj5mfSBngMElPa0SQwaJ2N44aM8V7ucUCFmUyvG6YP\n47iaxfcGSlWr7DBkB4eQHa+rqu3cS31kwtEf6r69vW3OabaXcn5+3o6O8e6xHld2NX716lU7Eoe2\n9RzOHt7s8f7nKV8JJ3MYVjtYeabMALwHFKvmgWEPWPSiYNy7qc68P2fz/PssmF2L5K5mrPLdfjaB\n1hwoMwiwUzynqOdAXLahp2gTFCfN+d6bHXZdPVrNMfJdYGqurnTqeiB/DkCnYqVkSp751MrZGwMl\naHRbeEeP7pvAPnWk4YEfWE/ZM+A589QrvIuI2NnZWb148aKePXu2tiaLtvivN7Pk+9IoZnDCfezJ\n+JxOyN8T8PX62T5XTTYF6tXrz8lHNbPZz7xemN9gQy1s+H3Zrmnbe3wDX8wZh01jb8ObM+opJ2st\nHTfryvbOCc3WadLTD9nWBKnjOLbIbq+flpWejHM/Mw0cZH6fWcz7lOTH29vbev78eR0dHdUHH3yw\ntmEassc4vHnzpl6/ft3Os+vRxO+6zz1ui7fb9wxI3l+dcR9q6WjOhE+7752zPz1waYe1X/dKLmAv\nNg8kTdHpc9Y9m5yZKS+O7V1zMtXrT8/hS4eT1NgeSH9/kwIz7ve0Ptt/B4Ity26XZzG5BlD2ujyC\np6zVZeYd+jDT7hRY2uX3sYmM12Ru0iemp+nVs9Nf1uZcLj092PtzW5En23k7HowRO+wyA8fY986Z\nTZtqx6qqHziBx9MOeN0kAfJxXM5+cRwGbfcaT7/bzhft+3+5e7sQWZc1TeiNrKqsv7X22rt3dx9O\n2yNt06PQ7cXIDCiIMqg3ijDgRetcONMiNAPeCF44iCB4NTcKA4LQMKINMozQol54I4KoFyPMjIJN\ntx49YnOOffqsffZaq1ZVZVZWrcrw4ssnvieeeCIya/0clycgyczviy/ijYj353nf+Pm4bRokUdng\ncWeHHPd1v7eTScZ16G/lZ9zDddb/bqUXB/v5OsYLcsA08+oTxXt8+BMce7zC5u7urpSL4BJkD2Vj\nxhNLbXnS4OHhoZy9gf+oD0GAlFLZl4kyMQ4qT8wj7J/0MMSh6bNwMlkxcuO1E/Q3nnVKSh0DftYx\nOoRGHRIF9A4UaflToNHPFKLekWHTupm+kWPH69i5jh4dLuEdVxxhZXoYrOk4KTh0IFf7f18/7GNu\nNUDungqQ5oFR0GtsoNXJVKDKxpmNfERUET+OenFfKQDAtRG9Tj40z9u3b23/OBDvEke37+/v49Wr\nV3F1dVVAMfeByqnrS76np8/V+yR81Nr9dgBFDViE2S9ItRQ5iqfKSz+6zrL5lGdVDzb3FotCNwNJ\nLsvpiRENOUfkXJ+Ax8ZcdRGPeVTtiFAArLxW6EvilOSIHC2gcP2K6wADAEMM4HrjARocj+Kz2Wzi\n9va2ejXC++hTrV+fX61W8fLlyzg9PS1LCbmN7Gxif6ge7qGA04FxRwvLA3QNHFheqqX9FRGytXZy\nwKahr/NwDk775KK1PfO9fYmzHB8fl+WAPDPtxtwFoct4RNBMrcob/RAnjpOzpezMV87lTjaqMrhh\nMsYacOHXKjj7i2dwGqnaSO6PnHM1u9N75x7rCdBQ55kCAWir4onR2Lp73JacczVj9LESyyAH37KM\nM/e/LkvNOZd3ZDq9g33Z+M3OIfMmrkPn6ayjBtDYPiMvToXFliIeMw1O4Dr4iCcveFLDOZo8e+po\nQ7/oDCbKRl51np1Dqk6n6ha9nnO9zJbv45Acrhv8jz7nk4LdRAoHFBjvRUTZa8sz2jiAkdvNdGHZ\nLrYvYMYeQQLMaGKccVptStMZN4+Pj1W7FF84ntd+/RBH87NwMiNahw7JKR91ZJgJ1dHh8jnpbBKX\nN4Pc3AiF5tc6EgxNU3/rpGo5CkL42wEUFmoVOM47M1VEGwmOJi/XqTTjNwtQz4FhBafl8xKKXn+w\n8dR63Pj2QKCLTmnfcHv1w04m04A+gKLkKBOAoQYuIsIqN7cXyxk0TjzLoZHWiEmhrVar7guNtR80\noU0AF4+Pj3FzcxMvX76M7373u+W9XRro6Y0N3wMIgbKE0ofxYxjr5G4fmFBjzm0qeejXjO3aZ3q/\ndxea8jjIVOuPWQ/09EdfmRNdOU/LfE3QieVg1mP9ANl8rY7YO9lm2njmBIA3m/5hZ6YJjMSsjXLO\nkcPrLf6tbYCcMY/z/jttQ6nbfHhG9Pb2NtbrdfPC7g8xtkwTfj8+PsarV6/i+fPnzRLF0i95Xirr\nHAfNy20eyTf3J0fQI+qZhJlfEypCAVHxpdBiOBx37TN1zkTVueW1rlwii5KeqMgnPIIOp8OaNu1q\nSkRXoXZAIOs+BpHQnbwssi530EcdfMPOSETr+LEt5pOEuR6mEfKL5Zt8mAtoVbsV0b47m3sQtIxS\nj19Hv/k9lB8rMW5wbVVAzjoEY45+vry8rPKC9sViep8oO2PcP7yiQLEEO3voB3ZyIuaVUuA12F0e\nQ5Z1zFKjfezwudVGPLnRkx/H/1q3Pq9ta+1pbZtAO5+TwQ4XO42MPdHf6kwhOHV9fV0d5MO4S18l\nott9WPZyngIh2JvM2JADQ5BhjN12u427u7uyTx72AOXjdXUXFxelbZA9fMOWnZ6eWszAGFh53q2C\neEr6bJzMCO84uDz7jIEyppZXAZ0O+Nr9qzregVUt313vOZaqbBTgcp2sdPqAsg9gp3tTm3xbfbsU\nWCJpREn7h8tiMMzJzXRqu3ptcWCzV4+OYUr1O74UwPbGlNszMpTb7bZZB4/raozVaKE+zavGjKOQ\nrAi4XcfHx3Fzc9Od7RoBI26Lm8189epVfPXVV9WhAi0o7QdONHjES4a1X3nc2Oj1ym9XNzigjfz1\nf0erXqcLUhr9rspgd6pT1p7ksvccFTM5QAAAIABJREFUiva5Wl+0/ZAbENULICl4VXp0/FhPqLz3\n9O50nfos1bM6Krfg+6Ojo+r1Hq5PHBACz+CD9/nhPbLc1pGxfeqYgh6cNIsXrvM9DvBwNL0H8g+h\nR/NppH8GuUn4LkfOKjc8pzmP2SyfIfkj+BCtZFzRcjhUTmVme4RvuGzNB2cVSwTRT9jvpAAd8lr3\nA/VZR56tgIaXH93jqkERLv9QYKdYyNlv/c1LC7H3lmdq4IhjaWVElMNFMAsKvmRHNedcZpBTStXM\nMTv2bJN7kwm9pG3SZYwfK6l8OZvMSfcppjTPVOHwlYhaNheLRVmmrrqcgwa8gsqtIkLb4bCwU8j5\nOXF/jVZz4bcumeXrio8Yy7Bzt1gsIsQW9bD4TF/fF9BrcLJ5TyVohY3AJAd4Ff/hgCO4yEEDfg8p\n+ovfTwocBP3M48n8zYFCHhe80gYrCzTYALqx2gT2IKUUq9Uqcs5xeXlZsBqPA/alP3/+vHrvMsaS\nv3l8D5ns2Jc+Kyczonas8B9JGZ4ZU4VWgYYDpX1nrB/1V+FTOplGvjYCV5pP6XZlA1zhW+9zPep8\naXtcv7goFz/Lv3W2TpnV0cDXHQOzQ9HrR02Ob7j/3FIL7TceY+0PBtC9MsB7bHx7ityNCcaU2+oi\nSep4qpMJ43SI8VW+4wTlimVSR0dH5RCg9Xpdlt7wshjtU66H+wnfGp3VZTt9+moQrKsa5nrRlqpl\npQyl09HdVp3o6ZkeN0uT0jyLiXsKstrivQHV+yz7Nc9P9al8O9q2W++kuKi18qXTadvttjp9dB94\n7I1vJc/N3TofordOJ3EfqJ5jYw5ds9lsqj18GtAZp76jqfoJZT08PMTr16/jyy+/rI6sB1hAcAeH\nRIwizGorXFKeQ9+h32anZw4OWHnatZb5LIIdwnoGsiqDZir7HTblm9sE+ms6qkfkWo5cHFkGaDnn\nohd5DxnKUF3GssYElrbLGLQ8P/cBL6dUfTm34WnnE/C463423lMMuQBvAeienJyUpXXIh0NK2DHi\nWaLFYlFO8uT3U/JhUbpPU2dJHQjXe+rgcZtZDpiPP1ZieXMypzqFHTv09f39fZyenlbvKYyYl1vy\nTKUuteVZNu4/1MfjrbYvpVSNBa6hPLfck9uiTo7rG05MB5fh8FRKKXJj+yf76fL36mU5gv4G76rc\n8oozdpZ12w749e7urpSF02I3m01ZrspvwuBgPGhB/8J5xUwn3peJ8eVnsUcXfMWzyrADFxcXcXx8\nXC0PR2B0uVyWtyGwLOBEW3X2nW/CH6bt//dOps62jAAW/utHryugcKAuZ+9s9ZJj+h6Nek8HUIE4\nlz2RUTto7OA4pcsC3qt/RJ9+O0eN+8k5ZUh5boQFZ5y/N6M5ol0NktKtz7DS1uVBvTYzfbysQZU+\nlAGUScS8zAGbutlhQtIgQem7gZF1PAYFoBEyJLeMaO6bWbG7/ke5iNgtFovy7qX1el3e48UGxc26\nV79TqkAt70918su0cFQSYMzpApWpkRNzqEzvk6HWFZqdYFe+6hzPw20tKuMcNZ35fNwedgScbKsT\nuW+GoNGfA1lWB9CPTQS/VsXJMv/n8gCg9OAtzs+gl/sNJ/7pCYVO37r2j5Iz4Pj99u3bePPmTVxc\nXJQlTTDweLk29mNCr+gSe61nRAMH7xicT98139q2DW1d+XVAH+3rt9rBbH5HKsus96UU82un8IoJ\n1RcRbZBacUSSoFG/wnmeFsPhcApTKI8foHPqcvU3g2G0DTMv4AM4mHgvH+SCX2PDCTYhYl5mjVl/\nzBhjVoYTdL2dITZ0a53IozzPSxc/tpOJutkp52v8P6X69WnoF+57d8gY6yK2n9y+iHYJss6GM8bh\ne6wD2bHQ4DfycR+ynlCc4mwu16fX9Z723aS721NylX48o3KJvlf8gbbBJqB9jIlwXbEdfi+Xy7LU\nFQE/7h/wH2wx+B+vD8GBWQhy3d/fV7IFmeKD13gWGryDV7TwypaUUnllyfX1dfM+c/TVZrOpZmWZ\nZ5jf2D7pypn3SZ+Nk9lLI+O9z7ArgypTTr8jeoZOFfcIKPaed8ZfnQUHZEEXC2lEf7mWA8zqqHAe\npknL0BkA/FZnkAVRgeNEvt/P2Wv3IUvR9hlcl0/by4ZA+4WvqYLVJSfIA4cSws6CqXWnNL+mQw0W\n/x6NsSoBfg7PqlPZO3UPgGmUeMkewC2u46XHACcMUhw4iZheGaIGjg0I+Inb3CM+RXv4l6u39HVA\n2nuAV4IMnQ7C0xO/zFnDXPekzwcc4Tk388bOIl9XWa50ChMiDuSct7ptwawCO6aDx6fNN9YvM6Bo\nZ+JqndCWof/ZYQLAQDDEtQOJDTnnA1iAIR/NYrxPcg4gAMDr16/LEnS0h1+ojeg0H76igLunY9XB\ndTZAbYc+P92nsqs2tM6fzRhufFuWre7prCezWO47ms1zaXY0cdgKByK4n1zyGGCsJ3Dd4YypLN8f\nXPZUxOGBcJQN50ODGpidWS6Xsd1ui5MJeYDDiT7qJQQneBXL2dlZPHv2rJrBg16AfLLtZYfJOZ6c\nRrye86c7+IdncxR0c78yfoIjw6uI0ObtdlvNMLJTw84Y6kX/6Iwk6mP7h6CBew0O08h1RETlGHPb\nVGfyM+pEqnzgGvaB8mwj7jN2Yp7VMnWihfmHZ8p5VpJtDNOqM/05z68lwX3Qyv3C+/55XFgf855M\nOJo3Nzclz/Pnz8uyfS6D+41f5Xh/f190PuhkvK2HmG02m1itVvHFF18UrAn+RGCJZZGd5J5zqXL3\n1LTXyUwp/UcR8c9HxMuc8z+8u/a3IuIf2mX5MiLe5Jz/TErpVyLiDyPif9/d+9s5579yCCEMVtDQ\nXkTDKRLnZLHT4JSZq5uvOYPbU/T7ovMMThQg9n4rnT3Dz/3mnDruh16/KNBz7dO6Z+Axt8fNSLNi\n0rYyrfuMKJSNi6pomUyTo9WVo/0CBTJqG6JjHPHCf96f4cAdR6v4w0aLPw7ssvHj+1gygTSKRO3r\n95xzNZMZUS9p7TkLbCD02r661aC4cYKDWSnpQVs6XNXPMZDT+lrY+wCHSo/miTBHhac6+utmu7lv\nkRxAqPo46ezKrEN0DB2gYV7USCjuT+2oaeXx5HJUNxV6czSOgxtTfh42w/Ghu6bAiJcl8amyMPAM\nInrJqLamDU4XPD4+xps3b+Lq6qq8wgCzqViSqMa/4ZkuTX0HSm2js3lNYIZ4qA4oUPuNTPTpG/+f\ny9oFgVJ7L2Xj1GZ/AU6W2iZ2fvq0tvJfES42uiLDpjoow0VFoE9rHq3GppKtup3gcQbu7OBg9Q1m\nMMGT+D87JzNN3GfsFF1cXJR3nUdErNfrakki7CPbU11FoPjK4RnnaLLu0tnTj5Eg9/goHdw+8BIv\nc2Qnj1fw6HsqWS+pY8gBA9Vd4APkwRYW1bdoi2IL/s96mm0Jl6OzxarLcQ1t5BNzNWl7FS+gfuUT\nYCd2CJnXwec6C6d9y7oP+bAknPf4Hx0dxeXlZfWqGextBM/BAWSZ3G7nvcvgkWfPnhUdz2OMGVKU\njwkLft0PBySAPTFbCn66ubmJ8/PzOD8/r/YBgxa18y5wyjL1oYHVQ2Yy/+OI+A8i4neJgH8Rv1NK\n/15EXFH+7+ec/8x7UxStsmGhcoLjnMCetXeAUX87J9Tl7wmBJjdAbiYA/9nYK5BQWtxsmZar9WsZ\nrh06q8LlsXFjgecolQWOQgO3QfNpex39qni0n9xMRY8efgaCpdddn6gBSSmVpQhYktWLBqmxdPnU\nGDiHUmdcoNzxbiRtu7a5N2ZIUHRwMgFQnLFVOegZINf/oD1NaKaaGeiNl9aVowZ1VT9Wz9bfWm75\n3b3XAroenfMYM3js6wuuCx8+ua5Hb851VNc5Vrs/u/zt4TGoi8GHOp4IvjigE6HgJJU+YsCl7dO+\n4rNVevpLHVdObiZd5VgBhwJIBZe8XG6URuOqMs/Xsdf52bNnsVwuyyETd3d35X1peIaj51xGjx7W\njwowRry7K3jHM7vxJEbmRwp7OS9QM7k67N9eX0pwyGXrPMo8HjGfssr38c18vVgsGr20y9iUP/rP\n4z/fI1fe9GmEB/09vYsEfR0xv3oL5QCMo50AtqB5dj7msrhc9OPJyUl5dyb2hsEesiypDXUBIW0T\n/9d7qm8gox8zqc3VoJOCf+hf7Jlz9hB9jz7mZbAs3znnao828vMMJes5jB+eUSyFMhx2dHq4h6+d\nPmbcyriQnd3aFqfY5vZANdXbjHvAr4wzuR0cTOEtODxbrHgL2Ab7kfmwIH4FDxy4+/v7OD8/jxcv\nXpT3T6LPr6+vq1PJsbcfPIlrcGIXi0XZ6wlHEjqf+ejo6KgEGXklCwf68Q2asTSW+yLnXB1mp74D\n+lL5fTRBcUja62TmnP/7lNKvuHtpovA3I+Kf+iAqYmZq/bAg8bcDERVt4UHzPpDQA8Cax/13de2b\nweN7DNoU7PB9V6cD8VwOX0dfs2AzHUozG7GeQePlDBr54PrdDIVTbjp2VRmS9J4CKVcHt633nCoo\nLgP7L1mQ1VDwXkwYJ+eI61gjuX5kJeAAMButj2lwWbHxMehq8JXH+JomZ3ypsQ2v87c6OMXQok9j\nzDO9cj099TLBHYGx0zK7e5yndkDnMltD7fop5/r1D8qjystsUHGt8tJK/QsBsLMOqIB01LpTnUPV\nR9rPtVFvnRx9hnWGtlPlka/zs3iG6+jpv55dYF0bEY18qVP3Pknlha/d39+Xd9BGzM4PItUc+Wb6\neuWN6u3xk6O35rednxU9v24g051UcWpKO9lnGjS3K+GwmlAueAd6DP2L60g6o5SJRpTDKQ+wgcrt\ncKUSnjM2wMnQdG8bfHIvt4F5hrEV8zMv33QBVfxm+T85OYnLy8vSj7x3GHmdjYNTr7LsZpmcnVFb\nyLM0Hzuhrt6qIT7zAGcwRERZ6qg2CmcYoM9TSpVzxMso2aFEf+nMHRKWQLOTyU4Inkdifc3/eeyV\ndtWReJ7pUt3jyttmDo7NwRS0XWf5UA7jU8VRPFuPQCgwF88C8752LhNjiGWlwFEYX/QvEpaDn56e\nxuXlZXEqr6+v4+3bt3F1dRVv376taMHWBzicDw8PcXl5WfiJT65FH/A7n5Hn3bt3cXZ2Vr3SBG2C\n43p/f19Om8W4gSeVB1TGWL6cPXlK+tA9mf9ERPw45/x/0LV/IKX0P0fE24j4t3PO/8OhhTHQ0aVM\nI4ejZ8xcPl6i4RS1ew7pUKOpgt17loWco3puYDVKpO2BgVBAhd8jJaNOFISYI/7cLyPQy8ZLn4uI\nctqfY1zQz4bUJQXprk8doNL8XF9Dp4BupsdF2xik9OqFQ6CAG4mFmvP0Zi/5QB42gHz4kAPU75Ng\nxLFfR6NovXHA71Hf8bfjp/l/nZeNmvZZA+rK060DWX87mua8eHQe474e4udAhcvL/KJOUy+pE8Wy\nOj3HvIy6c0yOZqpkHh+3vA11KbBFsEFBHevXLM5C258exII+BicjHhvp1tF9B1h5Dw5Age5FdmU9\nxTb0HMLtdhvX19dxfX1dovaYyQTQYbpdEEqBOX7zNeU9lb0x32l72muRTJ/zeNhyU+No6qO4NOrq\nHum1yNfgmmdA1PFD8NC151DXdso+1mlNMuOI/+zktWMlK5ukTsU/KJN1qdqgiBp/oLyTk5NqmR6/\nJxp52Oahroh5dYx75Qe3uweCcY3tHgIyHzupg6mOM69weHh4KAd3YSkyEjs5KJf3vepMXY9ngPUQ\n5Mb4wRGCc8T9zX2IxI4X14PvHsZSeQBfOflRneV18fy7t1JxhA+YHuZzbgdj25zrGT3se4+IOD09\njfPz8+LY4b2baAuXgXtYlorXhLx48aI4nF999VX86Ec/itvb27i5uSlBGF5im3MuS4qBszBWyHt+\nfl5eSQcZBebUd2fCdq1Wq7i4uGiW7eqYoE29gJCzvU9JH+pk/sWI+Jv0/0cR8ffnnL9NKf3ZiPgv\nUkq/kXN+qw+mlH47In47IqqX1O7ulXw6u0fP299IXNahAIAZvOcAaR29unuDosKgzO/KVCGa6pjL\nc04509zrt167e+BNnVjQze3B7xpotoGChjYZz0OYWoG2tpGvN8pzB2TUkDJwY8MHRc7tQF4AUD6l\nMSLK8iGeaWLwrAbAtU8BIn/UuUQdOAa7npGOyDl+PqX0d9DWpyQYzYgoCtEpIu3vHk8rL2l01PFg\nSq0+cEZUwZT2qfvdS1Nd4IXqTrdc0MJ04xmV0ZTqfUrOsO9zDNQhVLnj5zSgxEaaeQlJHT38ZiPu\n5LU4DYavp8OM/Cwjt32sB1IsFm2wRts21beogCD4TfmX+5YP38FHnTpHk0lF5r788ssu6AMdq9Uq\nrq+vyzsGcbAW61OA3n1AwI33PMM866NRUNA3M7mfbRccaHf31RFl3PaU3cgoZ/c34GDyksS5OKM/\nYnbcesDZwGg80dDjutrJO/O5+4+x4xmQioJUO5m6Won1kLunZUGmICs8A8WBRJVP5XeWNZdURvCc\nW82DmZogmfsYSfWAsztoH5xutpesx9yhNrwqSG0X4zudMdQPAn/4zf3N/af6tYcFOLCgvIfyuB0a\niOCJDx5/1jPut2JZZ8e0v05OTqplpqifcQrowsoQ5iWWi5OTk/K+YidreCalFGdnZ4UvcOgOyv/i\niy/i4uIivvzyy7i9vY3b29tyKj9mOXGg28XFRTx//jxOTk5ivV6X8kHn3d1dnJ6exnK5jLu7u7Jf\nNOf5FUT8nfN0mNxms4lnz57F4+NjOcyO+UJtg35Y5t43vbeTmVI6joh/ISL+LK7lnDcRsdn9/rsp\npe9HxD8YEY3A55x/JyJ+JyLi66+/zmz4+LOrq/k+BCAKwUw7aCj/ezMs4yLHzM/X+RkGaRFRASDk\n5zwsqLVz1tnPFG2fcd0KOLQ/2IDwc0gcPdPyuYyR4dBnZhzeOn9Kf89wadu1PwGqUkoFLLDBZEeN\n8/JptOoI6FI/Be+LxaJEmFTZMUB0RlnzMY295TsR03Kc6+tr1zc/yTn/uYiI4+Pj6sYY0E8J/eGW\nyqpi6iVuk5N5l9hZcPphKi8iBsuw+7ItS2FFbnRcURf+1vcmruq1v9Ad83Ih5xyUeui+m7WKaFc9\nwOjg92zEPT0MEB1o5r7m2dIRv5Tn9/CT1gVaecYw0cmh9ZiMy3R0u9STPRhqDiDtdzJtKjL3y7/8\ny1nlhuuFTF9fXxfA8fDwUN6P6XilF4HWfAou+bp+M3+rLdtd3GMfafzTYTN+VUBin+3d2Ygmb09/\nxNieYxZIA13ReY4dTXdvX9qPLVJpnpN9pB7454CB2jkG1PwMH0zSCzY4nuXEoF5XXinPMa2cFFP0\n6lXnEhhq52QWmUspvf8UDNWPGa+eLkAb1UZjZRH0Mgcz+AR4tAn3uB/YiVSHk516XUJb4R3ScTox\nUPhhylDpTl0ppAE5dTJ5HHlPI/I7nmA7xXzKNM6pxq6gB/VFRINdVb/qa2Uw8xwxO8Zw2NnOaftB\nO5aq4uArzFaCjsfHx1gul/HixYtqyeybN2/i1atXcXV1FXd3d5HS5LSmlMqBQ6gXTixmq3m2E23G\nqbRYIfDu3btYrVbx/PnziIjqGW2DOpTO2Xzf9CEzmf9MRPxvOecf4kJK6Rci4lXO+TGl9KsR8acj\n4v86tEAGBDywCirdbzCuCsXoOa4XyRrVqBl2vuZnmaxhEsWhA61CoeumR8avMjJi1NUIqaPE+ZQO\njXA5wYXBZ6FlY73NswPZA33cJ9q2vUB2l3Q21oEx5gfmlxHIVj5SIwxjwYqLwfhisSjKW/nECbEa\nA43a6m/Nx/SMHAHtS+aTUeKXCnP93CZ862wlG0Dt55RSAYlqhPRZlaNDk5P9HQQ7APhxOb1AVy7G\nWoMmSKrfeNx6sqlJjT4bVSQFiq6/cA3AEMt1FIhw/pGs4n/RBSnFdue0oCskc+WwMCgqbY0cedvq\nJjX+jganB7gP8e1kDWUosPzQpDOqTEPEtOTw+vo6VqtVnJ+fl+VS7qTqURt7No37julAH8zPtTqY\n9YSWrWML0/A+CY+VVr1HQXXcyNnzuc945od/9/BChk3bQ9c+nVIPGy/FLzkqPnW285B6WM5YL+gK\nGN2TyTyiegNOEdujnHM5BIiTOo6sA/Es2wq3NBf5lW7Qgfr5AKePmVgHuP4BzTrbyfLSm8hAebim\nuJVn29nZZEzJy2eRVMbxzYEApiXnHAszq+ywIMp2q+i6ZYsDjLZxu5HcVoxdzTvd0q76Ydllm8q/\ncXihjo/qNV56itfBMO9pf+p5FXAy+R74c7vdxosXL+LFixfx5Zdfxtu3b+Pt27fF+UQ5CC5WQaOU\nqgMlwZN86CS3DzOljHOdfezZFcZ075sOeYXJ34yIPx/TEoQfRsS/k3P+GxHxL0W9VDYi4p+MiH83\npfQQEduI+Cs551eHEsMGkJl31MB9xjOidRw92GyN8Ti1s1G9xEqB87u6KoFOqXLQkOrZzDq1OK51\nkN3smaZeHexosbF1RiilFMmMBRu6pnzT3qaNYvBd5FUFSftY7zng6nhRlSPTgG8cDY//R4tFZXjU\nMOtMKBSjCjmewR4CfHM0F8s3sOb/Q5SDJixLgcJyji7q7PGOjl8F5Civ9kfj1O08OR3zOl8Nfmue\ncvMRbf4e/+tzXFZPBrlt+B45LouO84SkUWW+rof8UCElP/M4AxbsR1EdwXKvMsVta8cfNE79VJ7F\nRwy9gmkFmU72nA52NKrsqzwqiPhY0VykHkgFvdvttGT25uYmFotF2eejy756NPn+rxPbGLZJvNzf\nO5uzX1UFUkpOr7NdXr2/q3SQi+uZ8ma9JuU5m1/ySNe4gGoNiGfXt4crGjqtnkD949l4ztfTESoL\nXGeFL6YbFRiHQxRRH+TDZY9wlN7j+s/Pzxu91ONJF2RV+96TQfAsn03wMe0dp5xzVY/qXtbN2EPn\nnCvtN+jbiNoJ1RlC3mvJhwn1ZjGReFYw5/nEX9X9ioUcnU73og6nkxWbM72uPK5X7Tp/u+v46NJr\nbhecUGAYDfDy5IDaiWb/Kik1jA/yoQ44l5hBhH5FPfALcHgWTqvFstqrq6tYrVbVYVJwOJ89exYp\npXLqLN55i7GA45nz5GhihlRlmPGl2he3feZ90iGny/7FzvXfMtd+LyJ+732JUYZQIeB8SMqYPeCD\nvPztlDQVXCyRgioFINN9YKbaGdSlCz3ljGsq1DlaQdcDXdBfZe8fZs+k7HEfRVRAWQy0MzrKrJq/\n104849qv993YqyLk5x2IVMXF/cyKEn3reM3NyqmDlXMuJ5DlPEdVMfbqWDonYQQQc543gmOvFu/5\nzDmXVx/ojJl3qp6WeEnKPrCsdGvq8YVzLNnJd88yiFXD00/tMi78rHmrrrMG33yggIO8tAyc+l/l\nw4G3lo46r0a+S6sK//iTJtlT4Hp1iS07Wk6WZ9DERdc6ojVOoqczOZtUBsCCGjg3pgoWFDRxuZw4\nD/MxvhmIj5zMWmc3t21+7iulc7vdxt3dXdze3sbJyUlsNpvSBwoGRuXoNdX/TjZgV2rgxzkmScv5\nsJm8XYXDfF5q6v+aT/Pvuz7WA1NicObxhNdpPXva3NuJf5HpAlYziCy/cQs6BePYcwIZe7Q6rS/D\nfFCd4hsG61oe7BhfY53Be7xRN9soXpo7tdMvPT0EAMPOur2SHzOBbj0HgcE8aIf9Zf19cnJicREc\nVna+eBx4FhMzlfhwQEhnSdGvEe3psWpfkVfHgL8jonKQuHwuS3WQ1s304Lrav+lay3eK2/AbfcGn\nf2swlPEb+tHZF8YavKcUbcGHZwV1kkAdabxfE8FC8CrGFEtsHx4eygqWx8fHePHiRfz4xz+Ob7/9\ntvA9XoGC16jwKgDUzYdxIWj54sWL0kc8Bm7m3eHUD0kfevDPR0sM5p2DoyBTr3EHcR78do4Ffjeg\nNedIYsj1PivAqY75PjNaC/bnPJqcw6sC7E44tMaAiOoZi/r35ChrXgWwTpkgnwo1lCcLpOZ35TvQ\n7ep01zl/771KTplGtFE/tBWCzH3PPIBxZoCac66iWQpq2VkAPWyYtA+wJh/vXVJDi3rPzs7i9evX\nFnh+aELUDbQ6498zVpzQbl62w4cjcR87fcAAhK85nnV0tPfrGcya/rr9+hznH/Ns6ywyvzi6HM0s\nHyibT/pl/ql5qG2P0om8PBMfMR+hzsaMSillMD8zAHYBDgcq0cc8rmzkIcsR0zL8EP52Oohnaziv\n8i+DRrfqYL8c9XmP29zTQ3wN7xo8PT0tUfBurUanqt1y8sP3+Zv15czvrY7G7wKoIpVsrgtyVQrR\nP7in+Z6SWl3Ay1FRY+3iphQdHufn2tS7XmGQzonqtrNM+bABTg5TMnzX0YGldsJbShfrWEczv/oB\n+XW1D+t3tcFsr3qOBPed8ifLJTuYHwMQ9xLK572grCNZ1uCMOh3NbcWY8t4/feUJHCjtm5qvWzvB\n48MJMq2TH26mta6n5QueRW10SkTk7bZ6z6fO6DI+VvpTYl1TO2+MzVAu6/tiI6hd7FRFzCchs03X\nSSGdoeW6mdewRFbtBOjiE4eZJzD7iRNt7+/vY7lcxmazKfr/iy++iGfPnsUf/dEf2RnNL774onoX\nJ8sH9nVuNpuCQ3kWFfynQVa2jR9jm8hn42RG1ILHDIVUGTVRRMr8uNYDar1vFRa+z9fmtf+pKHlW\ntOpMaNnWUOPadMNO/SsIR1KB2G635dCMiv5Jem2bev3k6ua8PG7uuhuDInBzBRZkO+DEfaqCy3k0\nMRBnZcf5ue0sbPyMizoyKI2Yo37YpM0KkTdgOwDL/cCK6vb2tqzT5yU76PvlchlHR0dlE/k8HofA\nuP3JgQ+d0YyYlzHxHj8HsJ3B4HqqPu6AsX2gvjXI5UHOGdo/KgNFpxCYntsV1fPalpzz/FZNoVdn\nadUR4v5Afu5PljvwaLts7LCfSbffAAAgAElEQVQ+UgdPZa1cj1lv7XOaABacPnXPMFhg+piOSbfN\nLoLKrvYPy68LFDEvK3jUWZsPTerEOjrw2pLValXk2gV1HP38W5fYOpCq+plnZ3YjUvWX0+V1Ms4O\n2gkeGjzhZjGbGkRWtC0tXXUQaSardjTVpsxlTfn28a1er+18tM96opoy1Ubsa+eITiYG+KD3nGuT\nA50pte95BA8psNWy8Tw7BD27yngK/fKpZzA5gQ7epqK6A/8fHh6q92Q3OnQn567N0FX8nAY457yt\nvYyIyvZqufw7E850QVt1Pns6JaJe3YS8Ee07Prm8mufqwMzc1nYMuHwkrLTSGWa2ZaMgEpx97n8u\nF3VjFRkS/BUeb55kAT/oWEREmfVeLpdxenoax8fHsVqtCo6LiPjVX/3VOD09jR/84AexXq/j4uIi\ncs6xWq3i2bNnkXOO6+vryDlX7/EEbZhFRfCYt0XwpAqPn7OF75s+KycTDdNTqZBGYJyvOYFUh8c5\nNKjDMTrT6Ay2cy57wMrRVkV4phvlv9INJ7RneKr8QevNU6rAmdLkQWLtgKty4Gu63IiX0DjDlHOu\nra/eM3SiLFVwTBfXp4DVtZXBNS+bUpo0X0rzKWA40Qv/oVwgsBxdgzFmWtTIRERF92q1KqAT91ih\n5zxFxO7v76sX+u6ob/q4l0a8z+0aAXQXIHKJ+RRGkcezOvwqwo41vh1AqvnbBDqi5a1eu7WYue2z\nEfRAk36IMUZ/VoZ3BzRZj3BbeqBEAzzswPTahT7QWQktX2c7WN85x9T1m9MBcx+1M/euLXqNy1T9\npACL+8LxrkZwe3VrGvVtr61al9Lw+PhYnEyAFzb43E+j5fcjfc6/Xf/P1yJGjma5HjliZt85D9MA\n2nZ2qH6eaOz0Xc9m+/+tY1nX4Gd59Xu+v78P9tHF/VJsn5bD9MT8PlqdeXE6b3o1UEtDJU9R97XD\nED15i4hGR6eUKieFHQDuR/wHf/PBKuw4aNuYn9nWsdzwO2w/pbPJMsrvp2Y5RD7MOi+Xy2bGNqJe\nJssOkPIjByF5uab2Eye2qRGtPtUPaFYsqnmUB7hcDnQqTYp1gO9bXgrK53mcv1ucMyXM/iIv4xPk\nR3/jNx+ahOfB39jDiTGHLLKdQNn8XlTw/enpaVxeXpbxZmzIOCelaT9zRJR3cOLQnl/7tV+LFy9e\nxPe+971Yr9fFmcS7MDEDimdx8CMCGSgn5zq44eyQLgf/mZnJVCeBHRQeBAcwtRwG9UiO+fcBaidE\n7EBxtEkVpaNNI6JuOh51jEAiDEWvP9RYVJFQURrF3Kd636F+K4095cZKyy014PYo/Xrf5UG/KV3q\nGDpHkWlEPW55njrL6rDy8lf85z1kiE5BIUFpYU28OgGoI6X69SCgY7PZxPX1dVEcTBMUHBTU9fW1\nDXI8xdHsJa5bFRIrKbRL6XCApjcu2A/BeVWx95LK/lyH/Ddt0/YyT0NWaqPtnsfv3IBu7gttX3lS\ndA/6sid7oFFPj0PAbjQ/NAL0LqDA9bmZTAUFIz3G+SJaZ5d5gtvd09kAAg4YsY5Wp845epx3f3q6\nbPVkAnrj/v4+7u7uyv5u1Y09OrXPnX1Q26a/W92Rqza6cS5OXZ211/h68fRAlvtyzrrDP1N4gOqd\n87O7NTuklbwrtiiO9P7x7umTffzET+U0H/oS0dpULosdgxEdIwcS9z1vtH3COoBnMCOi0t/sJONQ\nOuZdPTmZ6+k5cCrH/PtTJrZ7HPgBTTw+s/6d6MZMF2QcDoniNXYm+dBI9HfPUazGhlbc6Aosrgd1\nz2OJ9xjP46ttV5zIuMU5oLB1jJlZ1xea0zxBwDTuGw9uA8pkjKbBCXaouQ07IqoyebkvxpsTguoO\nk6J+OJrYXw8ciXvsbPJYLRaL8tYC4MfT09P4/ve/X/Ed3rOJ31iaiwkP8B3a9O7du2JXFL/xtY+x\nVDbiM3IyNSmTsfJxER+OTuxLI0fJJRdl40iJu8+AWdsUUSthFTgVYAe0tD5Xh7YXvysFIHuqens+\ne4CFy3TOgC5R6yXXPve7B44wJg4Uc54RcHQ0oE0wEqrokXRNPm9CZ55hBePAIOdPaVpP//r167i7\nu2tAMrcTS2XX63VEuI38nY4nrLUPBDknUw29aw8bJy4H8spywn3lHA1+ng0pP4uyIxh81c+6dkXa\nzfQbo6V1jfoLxbmuZGXvAkwuQMDlqE7Bs7xMSA3G3JdzGYXQhsh26Zxr5777vaRjzTyj+ZBHVwX0\n+kJlinmHdZOW4T6HOJh1HxzWdhe8cvXi/ZgcvFJZc3rA6RVuP9OtOl2v8z3uTx4f7Yccuew/1Pvc\nndxfo0CHXGme7eYdDciO7+d2ueeV9lx0QOUisw9GDug+21X+g45o+wEBJ3UwtKz9OGYOelX1Dw6C\nAz2zjE552caCDtg65m3Qqyse2AnBb7UdVf9UNNRtZWeP/3+qpPKEGVnQj6Xt7BRz+4AhNptNFcBz\nelDxhTpEjs8qfZhqXuRn2Nl0ebRuTqyPVC8wVuLrjHX5GutBpa+ne0u/Rc2f6ujxNZTrln42deUc\niSYFeFkpjyMmEFJKJYCyXC7LDCQC//jg1T4553L4D57X4DDsAmZ8cWDQs2fP4vj4OL766qv4/ve/\nHz/4wQ+q54+OjuL29rb81iW96GcnLxpgdfuO3zd9Vk6mggad6fOArla6bHyZwVkQuLN7gszl4B4D\nXwWD+qw6Itw2NRb6bE+pch6+NzI+VX6JRro6dPZC+1oNidbJkU04FqrQHI18XWd4UG+vP7RflGY+\n0cspZR4nnrV01xUg61IQzDYqj7DCQ738LI8hvu/v7+P169fx5s2bsjTEgeHtdhvn5+dlBoTp3fVC\n0y/vm7hO/XA0W8Evt8vxFfqAv9VJ4HtoljO2/KzynstLxZU6nKHt8ZdrV08OoXsUfO1T5FwHR0+Z\nRgaAXKcDpKW8piYPXnVlgOtvbTe3neVcn+/1Gz+P31oOntFgRS85O9GbGTnUuBZct6dubpOzNfzB\nvi+mA/S5NmjEWfP0wCvn1Wv7AK88ETn7e71x312w/TP6P7pur7m6Uus4V+3coVgF0eXeruCKj5Pj\nFx+MadpoWzg/p/ytfOCWZE73xuV2MdXAnmJmEnLD+80c5nFyCT0GO+lmtpBP7Z3je8YcnzLto4e/\n4ZAxFsL/Hs5S51M/OpO5T+f1ZHdkE3BfsbfizR5+ZCdT8bdiIy6/9xv/q9crSZ/pybec+IBBxoKa\ntD2Kq9EmOJmMd4D98BxjGPD26elppJTKjCZm9bG/N2Jebsv2lg+wRBnb7TZub2/LybMR86m5m80m\nzs/Py0wm7qPdOnGn8qS28EPTZ+NkOsOLDmJnhfNzXla0quw4/75r6HjnXCK5A25ce5yhdnWx4ej1\nx1MUiZZdmGkXZQaT9srRFxpz1IrL15k6TizMLDD7ACfq6/WNM6auLdxnPYWy3W5rxSX90KsfBuT+\n/r7MTIJPIqJENFmZsqJURYf6+Mjsx8fHokTwgl6X0M6Tk5NYr9eVgpjbYx+1ZY0SyyMvqeCIlzO+\n/Cwnnt3kvmYlzWX2jOpI3vu/U90vAjT3OQy8zFz7h+vhNussJtroZgfqcmS6hMrnesBXTIfTnciv\n4DlTDQpqRzqIx0VBk+sX1SP8XE9Pc99F1HuAFPRwH/b0gPLqhxrXffzCydkFvg65wlI7fUb7UgM6\nKlf6X8dIASWusX7kdqoNa9rO7ErPSUHtzF2jIw5zLp/S973nWvr8dZ7946BtzrT15An1juxgqwdq\nWePnxhjBB/WUj5yObkrKUzABusadLYDEDpW2iXmWV1u4vlCZ5P8qwz+NpLKIujEzhYTZJF7yzvsI\n3amxTufqfU4jnTzd228TXXk97OT0ltJVnaVAediBduXxqjctU53viPGrb5R+7mu3ogrfbEtU/7Ed\niojqACA8B0wIZxBLWMGbcCI58ICzPHjSC3WB3oeHh/L+zZRSfP311/Ebv/Eb8fu///txdXVVyjs9\nPY3ValXoYqzPflIvOMK4jlfjfUj6bJxMDJwDDLy+usdIqoy5rLmj/VIwpoHLUiOvTlVPsTsjqEZc\nn+/N1JVreTZwi8Uitjk3h6G4dik4ZyA/Migq4HpNlZ6jwy1d7gFSpg1Mz8uJXRk9+pk2Ba5Kn+Mj\ndgRYqXAEktfSQ1FAKWEdvQIy1Imy8Yzez3mKir169aqcGsb5FIyenJxUSyXqSF0L2rkc7jO95vL3\nZjFZWWl9ABLOocR9NQBap5OpNAlzOQhLjSplrvpiV3Jo37Axlyy+P7BHixxOlgn+9rzODmTfECPf\nRFZrKDEGiNRq/exkOJ2VqA/zfLFpr+eRug1V3SmVGZF9vLZPXnWZueoep1dUx+l4MDD9VFFcTc4h\nVNAcEc1SPOWhXoCH28oghfuJ+9YlpxMb0JrUpdrZVrOsnJ+RK4WW9rcEgWSpbA8kPyU5OdA0UIfm\nmVoWQDNEgfNPfa/56+f4mZTafleHAL/r8dtPf0/mWvmMHb2tru85E2q3er/Bq6qLma/VxsCxY/3X\na9/HSCpjKrP6XmxeOotrCB6xjVbZ5vp01lMxhZPNmicnPhvJi/Y5yu3lBe9ykU7/ur6v2kzX9NyV\nedZ3uzMjfXyLpCemcpsUu7OfwfzHmIz7A44g+AyrxlQnoxw+g4N5FHUgSBMxB9X51UAceMYBQKjj\n+Pi4HCT067/+6/EHf/AH8erVq1L2crksK+qYP3imn+sCbboS7WduJlPfpYNZHTdljeSAC34r2Juu\nRZPfCRNHLzRS55Y1Ohr4fw9cWwW4cya1DQEDjvZ06gSN3G6tg6+pcudyes6aCrzrQwgKz/LxbEQP\nyCj9yuhQvD0auX6nlJxjyfn1hbVOsUfUCg0nALIS0rKZF7FJPqJeK4//j4+PcXV1Fd9++22J8qoC\nZ/B5dnZWjqlWw/4RsFhJ6Hs+ZY0VFH9rpFllka8zCNYlxRp8aWRMaHTtJU6t/wm4akpj46YsTvgQ\n5jLl1N3n5JfotE4WG8FZLqYKHR8iaXSWy+S29vpR0WgGMiYexv9aJ8yPNvqGn+Vyw+u+lBaRswev\n/HzmMk0gYJ9hVKdNP59y2V2vTtxj2WE6WF+NQIADq5x6209Ux+t90IDbXfsTOqP3PgqI6tk5N852\nK22OZveMtrNLRfVMoaxaFgvArXolYj5AZerbFpfsp3sOipcVM9NDVTkq07Mug3yCNm97Xd+A3vr6\n3A8OtE9tbpfisd1nTOcCOjpz7niU5YNt0P9Xs5lIoAVtODk5idVqVew7li8qHnJyhu/RCqaZz4BJ\nc6S0/0ySXl2j+6wfnE7uPcPlN5grpvcdq13iergqxdHsHIKfVMaYPznAz8+gPvU5uCxMCgAfAucB\nr202m0hpPk0Yh/RUfUR2ExMDKc0nwPJMLjt8vOoJ77zE4T44h+Pm5iZWq1WhFfs4sa8TgQ3mRfxm\nO6Sy9THSZ+NkcmInU5UY7veuceqBMjWoyuTq1LjZVU0jJ03r6Dk5pS2pdUrVOQNdSpsDEb12KsB3\nfTeik8txAs6M65aljpQWopRKv5Y9Aq3aFu1HzQMhd33I7WDh5NkS0MzHWPO4gK9d/XAkHx4e4u3b\nt/HjH/841ut1ZWTUIOH3+fl53NzcVI7erlVNn39IAgCHAmKHE/WyomKAgd/oJ+2Hqf9rHtEZ7Z4M\nzaAKqQap2gbm7X1GFs/obwcYK8dT+KaS70X9mhzltx4Q5edHbdM9yBg3J8+R2kV+pW+pDfyf289g\nc6QfgVDZqLksEe0KB3yXfTfS565M7ndXH/Mmf7cy9OmSGnaVFd3zw89wm7QM/kCnIbCFMXOzvPq/\n13etzICndmMojj+z6T5NNNWp9cwFueedTeI00w8gnqr/5ZmKziTPTL9znpzoOZjEdfVltqUFIkFK\no5NQFsu9Ooeav6K3ZGtnKbm/Z5rwDPTSzGfcDp4pV3pYN3Dr3MQBO4fqBCgPan5d3jjUQR+YlA62\nabiGV5mhLeiH7XYb9/f3cXp6WgWiGRs9BdNoHsWHSK1tjOo5vbb7UXN1Q8IctDgEEys9I6wJPT/z\n/FGk1G6hwX/YUbWlyk8R7Wt18EEg/+TkpMIHzIc55zJLyBhxs9kUxy+lVF4ht1gsytLW0uacY0GO\nLGgC3VgWy3VyfwBb4nRm4MzvfOc78e2338b3vve9IgNwKo+Pj8teTQ7IYB81+pMnB5zd+ZD02TiZ\n7969a6bMIYA1EJ0GR/ceIakT45aJFWMoyYEbFuD5ui7liapA/HLLN7iu+U9EWvQNxug6aHPrzPXd\ng1rvCFg7YNsD244md41nZ/h5ppGdFUe3W+bLRpjHCwqElyO4pLO5CqA5H2jh38iP9xNBGWiZUGIR\n0TjdUCI3Nzfxx3/8x/HmzZtSvgPIoAFr/9frdVEWc7/Y5g7TyLDx8jz3YQWlYJhpZplGe6aIW93X\nPC4pLcp9XpLXANE9bWNDirp7cjrRXGW3POmWCCo44nHmqL+CBQVXypPaFpWH7XaKZh8fJ+ugsKz1\nwTg7yTPwDLQ2RXEa53rbWeuqP6KF0i3YzaV85hW00+l81TkO/Cr/uT5RI3uogXWgaV9yYJVBMtPJ\nM+DqFCNpm7geBrGgl/Wa0u/sXw8Y7n7t/rftrJ4R4OrSUJbped/ffWetzt86hZVcNYE59pjxu88X\neVo/39CW0vzNMjX/Z3nnOtH3LRYqdZpr2vYpS03/XGdrs+f/rcPD36pLNE9IjzFoZocAIN3pxJGM\nMD+r0/cpktMf/MHrJFjHo338rDpWnNAP7OQgcbv1+X55ddkox+Zn+1jGYeZRHedpjAp1TT1IPHHE\nbWG6Wc/Ndc2yoLqI+cXxDpeNMvkVcezEYQaS69BZZOSDr6Kz5+/evYv1eh13d3exWCxiuVzGxcVF\nwYIcZGd+xz11mvHqER1bYEXcPzs7i+9+97vx+vXr+OEPf1jwFOwl3qkJ55TtCjuYiuM+lix9Fk4m\nBAcMAA8cnc/LBVXZuWhaD6hRjYGonjL6CGQzE5fnFothdLUHQKo6Uquw8d+1m8vQPlBFMAJlvTJd\nv6mwOoCoyk/v8+yNK3vUT2ir4wWtC0Kis4DcBu0LVlTOEChNKF+jZG7GluvnKC5+Q7jX63W8fPky\nfvKTnzQRTu0P/D47OysnlH3KWcyIKNHYh4eHasmsLp1lRabLgpxx5rFk4FEbFLSqdjBrPh0HZGow\nOSW3lL2Wl9mXsrKkGJSSOlwcwXR0sVOE/xokcjxZy2YdkOH2NKAw9YF/rRdYnncNLvfnJX3zczUQ\nL/WWzqyBLsrBjFHOs6PJM7AAKvvApDqTrCf1fm/2sHcC4YckpZtBs9ISESXirDTqb9eWnl5HuU72\nXOrrZchk62jsmZjrpHpZbJp/DJzT1uFr6T1M/6k89fPt2v5ey4BbWllePE6JYPlTmVZ6J19yNLtU\n3x6PfZs3UoqgWSPmI9ClQB+Fsc6taCZ72uNdlk2sosFz+Mb1T5nUhuk1Dmrz4V16OjxjBW2vOlKH\nJO1Pfb5nM/A7s/A9Qe9NZcwBSXcfy0wZW+knpVTtO0Qfzm0h3ZJ83zMeGK1shAPGy17VAdb6+Rsr\nx7i/ccAPft/d3cVqtSrjrLofjipoZD5BH/FBUsorsPcnJydxcnISl5eX8d3vfjfevHkTr169Kstx\ngR9YPpwzqducfmadTHQ871nDVK8u99IlF+oU4Tq+WZnygI8cHHXYdldr5bsTTnUItRz+rstrIzmc\nt7eUTgG2OlBcR6+NEEaulzcjo4yeMdHfvXEAoysA0jYo6GFQiMT7Jbmf1LAx+OL/Wl5PwVtDGTOv\n8Tp6nYl1M2MaxeTl4Pf397Fer+Pq6ipevnwZ9/f3zYuaI2bIweB7uVzG27dvm9mX98Rapc0OeOSc\ny95PdjRRN/+GgWWlxS9sVp5S2UTQiccgpVSdkMz0aptbx7M1uq59vj9cuVJ8RLUfs9Ixi0VkcRZR\nFi/3Qv3oG+4ndrR6zimuOT7H/0pnNO2sZUllU9N0zcwGOSBb1TPVXuMa5oMWyGnE2gW7Iuq9r/od\nUcuhGlzwKfPxKB0KADkpuFKjzoEi6BmWbX1WDwfSOrhe1Y1owz4n09sO+V+vNd3VU+fjeAyX35aZ\n9ILNX+VtczblKj1jpVg7sOBZTuiyqvxmP2p2zajKn+Uh072qpl2+Wuf1xqwRQH+pKavWDamcDVHV\nk+tT/NWesy0EdoiIyCJLrMfYLvZ0ln4Y1HM+ldluH71n6tGDeqB/gFnPzs4KrThlVulSWe05R5rf\nYSANZDpMCh3KdFTlm3b3MC0nxUKN3s154nqjm1jn4ZuDaBO9qRy2p20ZBwVnO8k0omxMaDEeBn/q\n8lt+BybeiQkdjFf7wKlbrVYRMb/vkumFrLCtx2FBbOs4OMHbsCKmpbqPj9ObDE5PT+Ps7Cx+8Rd/\nMW5vb+Pdu3exXC7j7OysOr2W+xXtUlvEkwYfI30WTuZ2O73z5fz8vGyYZYXHnr1bhojE+SO8I8od\n5/Z68X8HAJHNLdtkGpxxnrJ7R4yfrZQzKQEnvAy8kK+nyLh/OFqk7eZytD1qjJyT4MaEFaA6nbjP\nbdVZHZSr4NIpZ9zDS3L1xE11ItlQ6LgooAUNHKVkgeXvHgB2y2BWq1W8evWqRL84FbpEGVxcXETO\nOe7u7mijdtv3EQp+JNV4qpvQpzi6G4rLLZtVxxLtd7MtdcSy5n9ufwVSjfyXlibHh9rguuxRqu/z\n71x9obwIOXyGDL1+AC4dL6uMOQeT87azv7RXaUc5G+2+AwlwPNM2j8fYOe+CX6sr22yOHpZNBgHu\nGQc6nZ5hHh0t/+6l93Ewe3S5mUjk1dkZ5GdZcs9yfrZnoJ23p3B7nM4ft3Xs9GVy7Eq4pyOz+rtf\n676+9/dLHSNVeACYZuex7m7021xfF/d26m7L83ZVaSv2bL5p5RCXVE4r275bLZKK4ws6Zr2g7yVU\nZ0WxAhwJXMNkAsug2lMuz6060ACN4rtPkVR2nd6JiLK/7vLysjiZ2C7DH12S2dPxXL/KpWJAzoP/\nTJcvn2VwV9f08N4+UTzVa0/kOQyruCvTPW6r0u/41m3D0pZon7GjxXSCtzm/s+nYn4lnT09PmxVd\nePWczpIiOILZRk7skDJe1plP2HrGuOfn53F5eRlff/11vHz5MhaLRTx79iwiorJnujzWfVDux0if\nhZP5+PgYf/InfxJffPFFvHjxogDox8fHZiDQQXA2YYB5mSIDLlU8iBioI4ln+RvRiZ7jlBaLSgj7\nxhhKup016yVWzjOzR0BgkFg40G4HNHp1cD37lDQrCC1XgYn+ZserN4uF8USkyNGvQsb3FVBpxGbk\nnDL45utMn6MZQJBf+OscV9Ck+02Rd71ex/X1dcVz3F7UxW06OzuL9Xpdvcx3eqY7hB+U0Nb7+/tq\nNhPfzujrMig10ioDuOcCQD1XD/mqttP/QxyCmR9aY9t5ojyHqH/dhn5ZNW/M9EN++X9EfWCB6gUu\nD8+Bz7hdydSN/K4flO/m+nIXdzi5Yjl15brE8gU55uXvAAJYXoU0msVw4FRnL/nDy1Q/VurVzwCa\nZ3Ui5leZ6HImfLhcdThZ9tBvsH9YicF9zr/ZNvZ4Yp9c8e2O29fk04ff15lvaDgokFZ7oR7ko0y2\nAdAzDAdYv890QOZZP/DsDOHxinDnmHODFHzrtUzlz7/rsZ2dgTkA39qVuV08C6OJ6WXgiut4jzRv\n9XB4RO0Gg2XH7596uSxoUEzg/vMsFC/NVD3JExZultjp66fIBT+jwbnGbu6S4yW+Xv5PFy1de5d2\nq24lG8z5F4tFU0+fzkWk5vTn2vlHgIQPH8w5V8t61XFlTInywGvsI+BcDra/TMN2u61efYIzNZgO\n2DfwC+NS0MF8gsm38/PzuLi4iC+//DI2m01st9syk+6CkrqEFs4sv9fzY6S9TmZK6U9FxO9GxHdi\n4qnfyTn/9ZTSz0XE34qIX4mI/zsifjPn/DpNXPDXI+Kfi4hVRPxWzvnv7avn8fExbm5uYrvdxnq9\njouLizLVu1wuy+lPbPhgNPnYYE7q/auQKXBzaZtzk68SsDTNER0q/D1wx0aD66idYBi0qf2IkiAf\nOzkoR4E9t12BRU+RMVAd2WkVFG2zAmOtnwVBx5nL6tHn+hdCyMutGzBqaHdLtNGPep/bgGgsg0V+\nbpexqiciyjJU1KPtUIcZy8jX6zXtUZmNRcVfT8RpDrAgPT4+FmBwf38fm80mzs7OqqWzDIgVCLhx\n135So6u0Ke/aNtBvxzNcnv5msNX23bxHpPB11EakztfKWsRuPLc50sIfUw9DozzulD8MXi/C7pox\nGovdv8j5sC0FCpqY3koXGRrmPH2wo3WwoWVj7/qFv/fNXoKHwd+fArCqsVf5wKwz6N5utyWIwzNB\nuK9Radb3upyO+w51qq1AuU5ODnEsm/TE/EV/1Ve7+Wq2S8396nKHFF5+P8ubqdACb71mymdVXPpZ\nKpCiZls/3ZyrzvLQfN3Zw5Eu11RAdKrtzUzDLItuWSbX6WYdkQ/P6xsEOOmzCo65/J/mTCZo4zbx\nNafnncyozlScgW92QHBNV/709HPO02t0nM1MVkBqneywaqMXpovV80/REWr/ubiu3aT8jOkc3+Me\nxoqdzIh5Dy2wNHCV2g7VDzq5AxuO8iLm4DAHV9imsA5mnayrBJQONzGGQ4nOzs7i4uIivvrqq6pe\nDl5zUAYzlnAwP/ZS2YjDZjLfRcS/kXP+eyml5xHxd1NK/01E/FZE/Lc557+WUvqrEfFXI+LfjIh/\nNiL+9O7zj0bEf7j77qaUUpyfn5fjdln5oPEuIg5AD8bQQesBSBU6Buecv6ccSlm7/+w0mNZVShpl\nj4SxdhBap4wFpicMcEI1r4I1VWw9x80tT+vR75yknqLi+rROzcdjqzSrYmZnFUaI28t9yfbdOSTo\nRwikAnSmB5vKUTbnLwDekAQAACAASURBVA6U0I7Zht7pt1wGlMP5+Xnc39+XpbJTWW3/PjU5pc8J\nkbi7u7sCxvUgII2cuTZwJBH1MkhOhu+1T2pA6KOl2rZeGVbejU7QPsEspgKcFrSaKGzkiG3EY8Aw\n+Zn/iHbZNl/j6D7LCTW0/GQ9oHXU8maA9iCxI9hzOKc2V08193vlRtT7naEH3F4c1SOO99Th1FnM\nfdHcD5GxHnhW4Io2875LDdgw7U7menpXx1/5dPSc8sn0Q/vHXuYcYnNpmWzFj+rs+npGabhVIPaM\nJdn66bef4YlgvqO2pTTjhGj5seprOM67mU0DAwKHYk2/++1p9NROnvlyKyf+nu7PZLuI+jjIEVEv\nz1O84eywC6jtczSRNMjyqZM6mSq7kMvlcmlxiL6yBO1nHMGYq+eMMj3OIeRn8O3tqY2mVnykPLXP\nxjosN5WB+iKmd3rWs3UjPaQJfTTSVxoIwDNYCQm+Wy6XzVJVtjfcHixNfXh4iLu7u9I3wG8oB7Tx\nHk7Ux/yr465tn/tu1tkI8qAOTMIdHR3F2dlZfP311/Hw8BBXV1cRUZ+1gnIQSNWZTXXYPzTtdTJz\nzj+KiB/tfl+nlP4wIv6+iPgLEfHnd9n+k4j472JyMv9CRPxunqj82ymlL1NK392VM0xY24wZy4go\nA4OBPTs7K0wREVUnKUP2mNQxM9/jZ03M0Qp0PUM6v25B08w4i6rkRI5oRVPO08EhwnwsDOzApJRi\nW0BZn2m5jQCmCnD2ASmnUHAd4+EAny5H4KQC54ILKB9Gxe0tQlncbnZSnRJz7WCaetEy0Il72GvS\nE1QAFtzX6FrPGMB4pTS9DgVLZae6Jh7a5yiNUs+xVBqgXDebTeVo8oyLzmgqSNBoLMpWeuhPla9v\nMHd5qOkOrGqdI6PpxrzwvjqYLLcdMFAZ/ciRtwAUbfu5fjc27HBALvpgYAbJ6BONpCKfOh7c577s\ntn9GvLTPsew9ow7UKJDB+dU5Y+ecnUu8gmg0k6l69KnJOZb8YVeGAbRrgwJxljPeMqD9xHS41ymN\nHMuR3E3P1/2k/cZ5WmfTP9tmADH0P8s9+0C/wEOGs+LrpJGY1gPmKyM5FkqqospS/I7jOZWpHTKu\nIdyhPp2ku+XwCOwiB1/Z0XLA3/GOy8e8jOeUz90ZAB/rBfK95JxedQ6gU05PT8t7D4EJ0EbFPrwS\nTzEe26ie7vH9Wv8Z2susK15mHpmLOGxypLKPjc2b+WfCK7Uzp/iPSo/adtXvSHV2fLpW8zpoQX87\nLIzEgV1Oi8Uizs/PS1n8OhIOKjB/AKuiXsaf7HPwIUSK97k/MOsK/r+/vy+rPi8vL+Pk5CRub2/L\n6lDuG9CkWI0DlqMA61PTk/ZkppR+JSL+kYj4nyLiO+Q4/klMy2kjJgf0B/TYD3fXhk4mOps7E512\nenpavXOGO4efxW9OPFCsBPh4X6f0mGmL8Bng5Zfz1eXMzMGWsKfka2NXAVIjSCmlKdKYCHxFRE67\n+CPskzh7Wj5HXTQdLY4ikn+O+5bBJc+yIulsDDMyK2vQqMqDnUkGh1hOzeUiP8pVh1CX+ijwUrp7\nAAwOMx+/fXR0VJZN7IsM8T1noBUkPz4+loOxVqvVzsCLQZG0L5L/1AQnkx3NzWZTjCoOOYDSQpCo\nN1uDdvJSk8bAUiNbwLtrJcaGwJ0DzJAxC6YjRVr4/U1Kb0Q740gPWJlt5XC3L0t4VOtyIJ8DLaBl\nnwOgQDjnHhBo9ZOCZOcAs7Ht9Zk6ijNd7RIyrV/b52ZLIuZDthSc9owqB0p4n9i+9BRH0zmXfLCP\njh87w245LP/m2UzVnSMHAPs2Wb85W7qfr5qO6cyPBF2tPSYTstAi5+QcSuesxvgh1yTVpzWHzpig\nKqDooXGayj4UrE80657vxsHulOFszkzuDL4RpORnWRd5+zItp+cZSyS2v44P8Rt8W+sez/uq61SW\ndBb/UyaWL5VFJDgeDw8P1SFHTqfwLKbrB4cztc84/2ivtXu+YOS6hnCBF+fk9rClpVdsM+NzbXdd\nbwTLrcrPPhzPebhefWera6t7/vj4OJ49e1b2t7PtYJ0OG4N9mCcnJ2V7n374zRqMk9meqp3l4AVO\nMMZJs5hNv7+/L2VDJnnWkm3hp1gRcLCTmVJ6FhG/FxH/es75rTBZTrzb9rDyfjsifjsi4vT0tFI4\nJycncXZ2Fs+ePSv7MqEMsakW9fO+RGUojo5UDEfMgrz4dsDQmYIeg6vwjQZLhRMOag+cOSGJaCON\naUKJ1V5DnmZ3NCpD1wW2bVb654x7orSiWLQuNUQ9JckRr+12W62ldwqQaeW+ULq4XgVrqsxZ0HV5\nkBocVv48k+eMlPuGMoiYDk3AS3+dgZ4bVXV8RMTPp5T+DvI6xap97fj38XF6D9R6vY71el1WGOjy\nWXdYiQO87CQ5/ub+5t8qq14OPZBj4FTyEiqeyuYxqIEWAxqmV3WHBlva4Esu9fT0D7eP6dHI7qid\nrfPWtkf7EIC4Lq91+vppBrKan2ULs7halss7098uzd33vIJWdS4fHh5is9mUwEmvbU9ytkjmnj9/\n3szoq7HXlHMuDiS3w83m6Gyn0uvodvrtKe2r+YgCrAc+r/lbYHcwKfMzTe3GfU31ff4/q47U0JVg\nV0MCG6ndc+xxAzmaOwcyIui3OsA73eJmN625bT3QWo/yPeYL57UzHe217dbP6jnbwcAZPMeHjPAh\nLCmlSi5Ubzj+50+QzH2KpLbMOZ26xJ0PqvS4b95nzkntoLvGZajtVAy7H8PNSe1jjUtaB3S63ceA\naS6wwVu4P7L7buZS25Agh+Vaf2sIymtWLxIm1hV3uAZdu1wui+MIZ45XwfBqL5SxXC7L8lzeC8oO\nIP9mWriPeKlsRBS/CYcJ4SCg1WpVlgjDKWZn0q08+5jpICczpXQSk4P5n+ac//Pd5R+n3TLYlNJ3\nI+Ll7vr/ExF/ih7/5d21KuWcfycifici4vnz5/ndu3flvTMvXryIy8vLMmPDR/7iXTHMIHBWHMOo\nY9JzWnq/+X/PcDOgbQHiLHRsNFz5PePeo8fla5TGoF1oEwsmnrfO5r665J5VdkIPC6yW23ccojh3\nzAMqgHr6JD/LyxrQD0qztknBMtOMduA+ZjHxH/wJIQatarAipsOmFlEfGoB8iHTd3t5W9xuaPYv8\nJOf85yIijo+PR3Hw2BcsuL+/j5ubm7i8vKxmNO/v72O5XA5PxFSgzUuFeQx6Y8FpFIGcxsy0LrX5\ncs6lz2a29s6g0qhlMR0AhyNZUT6HYePVFrhey2Qb9ChU51rnlFZU1+vEwY+RPkTekazMxrmud6Z7\nvq5GnK9z0n7XMWCgO3LIensw4WBuNpvuEe61Xj8oFZn7zne+k5lGdgx537j2l85iurZqmdATI53G\n5egeH74PJ+rg1OGJ3S+Sv17+1F5WlVScU3XSJIOU64HzHF3KcJpTu9S12MleGthebsTIdrb0I3+E\n1cm7fpnliasf2eg57wgXOFDPeZBYrzv77wIoLI/Kcy5o62RGZXr3TJG59MTJj0MT6HErJiKi6Ba2\n3+pE7Ohr+piD0fvsH8tmiz0Pa4Ni1RkXeb7Y/QrsD9ax6yXdpqQ8oXjR4U+cHqv6zznCTlf28KXr\nO+V7pp2XlfIsJMYYgYaI+d3uEfMyVzia7CgCQ/LJshgLXh3H+ID19fHxcTx//jyWy2VsNpu4vLyM\ny8vLuLq6CjidaKueofGpDv2JiOhPZexSmlrwNyLiD3PO/z7d+q8i4i/vfv/liPgv6fpfSlP6xyLi\nKh+wH/Pi4iJ+4Rd+IX7+538+zs7OYrud3p2Jg00wEPzeGbeMUZ0O3GMG4eiECmRPUB3wqh3MKALH\ngq9CyM9W9Ug7OG/vulP+bBrTzlAqkO0poZFT18sfQTHQQXbu194MIu7x+PFvpQcnaumGbSgBVdz6\nPC9Xc0ttFMArv3F+Bef8zbNXajhYAZZ+IqPllOlqtapmWppxegIedImBWI8HHh8f4/b2Nq6vr2O9\nXlczQDybqZvLbXujNU4jUKx8P+LrHkAq4xM9Xvegap9zVcmrnRWoI6gaTY1o+ce1k+Wjob8g5ZY2\n7ScN1tXjM44Yc59g//e++tp+0yWx9Zi5mTk2ggzImEYNaujeLeZPLHPCEvC7u7u9e7sOxHFNYpnm\niLIadwY/OqvjnEvX5iZ41QEP1az8YjE5WAmOTacdMQaUnBjEsm7Z/bCzLE/tXyolVAHCHM9ltrIx\nqrfAauiqqNvO/3fqe+bjwJh7urEKyekKplVxRIqefqmfc/d7uqu3LcThHA1sjPS7Oow8W8Ir0Uqf\nGOdN62FwrDMxnzq5oJXSijYg6bYRxqNTn9b4BfU4TIHfsw2rE+vdnp3t2cVRUprVPoxSSu3ruXRC\niNvYtx1toKGH5TUpf6sPweU0qx9j7jt29B4f59P2cQ8zm5hZvLy8jGfPnpU3ZmBGkW0XcCH7N1id\nxzPciie5HNSN7/Pz83jx4kVcXFzE6elpXFxcFJ5S28MB16fyxb50yEzmPx4R/3JE/K8ppf9ld+3f\nioi/FhH/WUrpX42IP4qI39zd+69jen3J/xnTK0z+lX0VHB0dxS/90i+VGZCHh4fiOJydnTVASjsd\n1wpTHuAo6XVlKKRFWpST1TxY2oUSTblV4FUcC3W2NA/X5wC4c3pdHhYMFqKm7JzJWfTlO3Bfnp0s\nj21PT4k4ekCrc+agqJR27R9W8JZe+l8BLFMuKwB1fpFH76FdvJYeAs0KSgGjS+pUvXv3rrwDyfL2\nBzuY6O+ar126v7+Pq6urOD8/j/Pz8zg9PS0fAPfj4+MizwweFJAoMDl0Vq0nlz0g1dxLDiy3hqVn\nyLT+ck1mD8EP+owzppOP2PKiGkQ2OE0byNFs3OdcR68BGpUvWT+NDE/PEDtZaHWZtjfkvuyPz/6w\nGu0rB1B1BoQP+0GQZL1ex/39/UFgdR+ocUnpgfPIe9O0j9zSQZWZiKjaqHoF+ZV2LafbIuIn5Gv5\nPuQ/X/C/HUDu39S/rsxc/R/SlPquXcSOn3pjLLIF+zfzcylE6KIZxL0zr3ovyzUVliizmk5cU6qX\nEpL57tpJZ/On/H6WkW0hr8TAclh2EiHHyvPshKIOvocgkcoy74X71KkX0GGnB45lxCzDZ2dnFT5h\nh4Fxl2ISTs7mjK67sn359Uyql52W7/Y5JYrbIubThLWtTKez14zJHC5merg87ud6EsofrKe6kyct\nMLnBDhvax7zLPAHeRN36Fg38xrO6mqmHQVm28Pzj42OcnJzE8fFxXF5exvPnzwvvqd1xs5g/dScz\n5/w/Rl8D/tMmf46If+0pRKBz3rx5E0dHR3FxcVEOc1FQ7oCeOpwwgD1wqoAvKL+CODiYyrhzuaix\n6YfqvwMPUaidyukBZKWJr+szOc8HBTil4+hSh0+/ObGwVsZn14jp0nj/HNPiBJsFSO9BaUPomEY1\nTgoUQQcbInY0uW6lUwWc+9zNbim/YK0+R3DZOCuA1PoVIDP975v6PFmD/16enKfDh169elWcS+zN\n3Gw25ZVEx8fHRYlxJFfbjzKt0xSt3DoHje/hmR7YHWHIER2oo/qNWA1+tLZ7poVQIHiD641odRq3\ni9ukupHrqVo95JO6XC3P6VI3G+10iZOVoAPJ2naNHU297nSEOmSjZbLgVewxvru76y6z7/HbUxLz\nO4OP0cwpz9y44IzObB7qZCp4Rz49HX0EfPGz4oGmBWOHz/bnwV3cz1gXm3o3IqKvC1RzZPmu7qXZ\n0Sz17AVsKE1L7OQueq1DpMnfk52pnJYH9jsq9TXYRoBdPUhKD+wDP3NgheWK7YHqFLaFKkv75Ohj\nJZY1fOvWkJSm1VTY3hUxn83AS9l7+pttHfrhkNS3ezPtTymn/C/bPuo8qhNyw5jtnnzwi4692j4+\nhbXCmgbzKk09nKxYbqKpzaM4UjELHE2kh4eHanYz51ytkkHgkjEoArksO6CJcTZ4mgOrvGJP+wqH\nTeF1Ji9evIjtdlt4ke2OvhngU6wEeNLpsp8qPT4+xsuXL+P4+DguLi4KkwGUYnrZMWREbQSdIwBQ\nxP8bkEjJGT8HVFMSJU6/1blyzt2OegvGmjrJBjkgWvUHRZp0poMVuVcMPYXRJudULRbzgSkquKPn\n1WlgJdRTIvw876Hi/lGhcc4g+ANlILgBwdXnuH5duoCl3RqVgoAzj7olN8jDRgaAuDeWc+fYYXpy\nmvt8NhI9Xthup2Xt3377bXE0sVQEy0YwkwlZ1g3mbLS5TTqbhmuqkPcZ014ezdejpQeGq/87rKh5\nlG9ynvbbRtq/XEd1iK5G0JlH1XNKIz/b5SFpm0ZRkfDbzRw4OdFxZZpG9TOdyi+aT50slS/IEU77\n0xnM1WpVvXd2lA4FfZycg7ndbkvwidvISZ1MN+vPywUdaEXZzCu8N6xuU+vw9Jwwp4v1Xl1esuU4\n5QV3le3ZeO5xLqdRjR1VmUPaZohLiYJCoa2ZyymOJrfG6oMI977LEUtN99q9mQgou/w4QXaUHDZh\nneXwk9pjnnVJKcXi6CiCgLpiDjyDQ0gi6pUPysegiWVH5Rzl/zRmMnuOJTsYTA+/0gyO58hBgt1T\n3el0aU/HRtQ6vodf2tTHxX6WvLWJtb3t41q2ay4BT7nntS2FRGMXGI8x/2l5jg4eW9a7KB/LWnl/\nJuvalObX1/RmJEtQpjPe6AfGjowPgC9zzmXlGM64WS6X8eLFi2rfMx/MCFv4M+9kcufx8gl42gCn\nPDgRM+DUMpyzgsSD6EDsIYAyZwBQFcLWwXSJFapTEE64K0NLAqTL0uwqGjIUPWevctBSqpZD9cDP\nKGl7XL3qCCDpPjV1HlmxKOjS+tnZc84G7gPwMyDFmnjOyw5lT4Gpc4A8vHaeDRW/egBtQh5eJ89K\nt+1wf/lDUmtQXJ6pjbe3t/HNN99Us5c4yIv3UsPhdDM4DCQi+gGS2VECjRhX3I/gCOqofZxGfNT7\n3uWKWuim/w5A4DdAFtqD68zXSofORCHv8fGkxic+8Q6Q0ou/TBfLG19jx8QZe5VTdST4Gd6HUj/j\nwJDXHaBHX+DOQIBnSzRyCwcTHziYq9UqNptN1/H9GAkyz0b93bt31VYFbSv0AC/ZUudAwZCbzcQ1\n9+J78Fc9xjyOefe/bxdLmVGrIyuHRo4El9bZ7ZyiT3ORezzNpt7aQUyMDZhOVwZ+CD/z79p+hdyz\npfVIPkg3dxPGSRxHp9uc7m2Lq/EWe81pR6gCdbaPEfUWqJ4OZpvJ/3sy8SmTHjrE+gfYARiW9Tzy\nn56eFnvOAWh2BrQ9I4esTTkQZJjtperRfjBGx32+3zq47h6XwYkxupsAAB+wM8Y8o3WNAiTaf+o/\n6FJl1YEc8ODyeZzgZGKLEDuWi8WivFIEb8VAXQjA6+E9uJ+pjXwoENtbPiSI24B72J95cXFRLS/H\nrCrboU+51PyzcDIXi0U8e/Yscs5lgyw6D6cxRUTVwc5Iug/y7It48PUROLWC1Gh8H91hw5o6v53j\nlVKaIpYDR60C4LvopmtjcSKl3uqeaa+CZK67m3b94hSBUwA6U+EUqoImponHWWmEgLpoFH4DfPGe\nD345Ll5VgjrYuES0Dq32G2jGt75CQQEfz3awIVLa3zeNxk+Bx778ERHr9Tq++eabqr+gbOFkYq8A\nHE0oeo4AQtErn7SyOS91AXBjw+lleXoGdTpeQvv1t8ps0x/FcWOc1coKL4/RxH2gz/d0HBsWBa9a\n9lRWWyc3otd/zphpX0zfObbbnfxSPh5r0Kvl1X3Vgk028nySnzpYbvZSly9hiexqtYqbm5tYrVZd\nQ6t9/b6J6eLluz23CfWBdjdTybT1ZjR7M91Of868Vqgo9DiZqiyG2I9eSsF8R3XRwwisziU+JWjk\nHbVae7QPz/5m63BFONnBda/vuWYuKuuM5JPYauKWovfMjC/KZP2IJ3uV1jIYdR1J93O2q7QUmy1i\n5wiSc4j3CLJ9WywWcXp6WgW0HNZQp1JlPqJ/uNXHTjqTyvSo0wmnEtcwm8lbf1j23D5NpBE+Vful\nvAke7D3f43l3r3H6OrI5sleObsV8cK6ULnY+FUv2aFds6Nql9q5XPo8P23LoaDiYGMvz8/OqPGAh\nrifVFVT9h1l/ni3lpbLAt7UdjoqOx8fpsEYOuPIBQL1JqA9Nn42TeX5+XpQNg1GOfACc4hkncM7J\ngIGNaJke1/h5a0i7gC1XDDF9G0FNSQyrvKfRLCs4JCnQ4GujpO1XI+T6wO0foAL9s8UQ1TZNwY8D\nmW7voUaV9FsNHmjhvmaj4PqEDQXy8wuVoSSYr7get2yHDSGEGsv1+D1KDihyuXZsP9zftKkPmPrp\n4eEhXr9+XZXB+w7U6UT/cURP5ZVndjnx+PRBIRvrCAZ2DAZmnq1n+Zo+6S4navWDkx8sGwZtvBIB\nSWfemfcZyDHvsZ60gYgd4pyu+Vkp7q9e+91smOZ349SAkuRmQOd7vTLUcdL/HJXlAA7PXPIezNvb\n23j79m1cXV3FZrNp2uvS+zqa0CtMI0e49z3HAF3BNvKxg60OOADucrkcAi+Vh4/R9vn5VjZGRfYc\nzEPpGIHqfp2HpBm4V1edMydLhRn0R7MCYverOAj1fziO6mjaRuyNAbtgGT/Y1uGArJNf6G0O8vAs\nSkQbJF4sFtXScXVimbd7PP7TcjJV1tiWQJ4xm8R7T3UmFjOe6KuIsHpx5OTgv45lK+NNKwJ2QGXd\n2VP9XdGHVwhlb4NV5nWVRTb52WYoVgW/cL+X9u5aorOgrk1uJSPzlN5zeyUxdsyHCJ5ofthO6GAO\nqLPcaJsxwQFnErTwN2Mt9KPyEtPJp//rjO3HTJ+FkxkRxbFkEAUgis7i4651GaMDdUjqYOp1l5cH\n3QE3BacziG2NJpi+x+jTBd8vFjDK/5qGbA1MpXz4edOeiPqEV1VclUDjXs7VFD8riinP3IduCUKv\nTZp6QJaVCRQ997Xur6wUHBknt8wMSgPr13kWhYXbLasALbqfyi3hU6cHiU/m0376VA7m+yTmwdVq\nFT/5yU9K+3ipB8t2xGxscU2DC4fsPdyXpv6sHRJ+fv5ujRDzQVoMeHKurOHliGgcawcOlUdLvfSM\nHhQ04kN1NJ3hdkZ9pHdQD/h3urYoPVCVYcrsybtL2h+QIQAMNew8e8kzmNh7CedyvV7Hzc1NfPvt\nt/HNN9/EarXq0sDt/tCkTiYfMjTqD3YyGXSrvuJ+cu3h4Jkbc4Ag/J9J6re9AMzyX2zM3MJ+xzTm\ncBTA4eve2ZvNW2pyVk/T/YYkAua5xJI7MtvhnRZw49pM9zwetoinpU4ZDvi3dj0COlILnUie50od\nb0EueaZFl+NBt+uySAbYzJ8sd+pEOp7/aezJZFrUwYRtx33oKT4ESJ0PtA+OSSuD3mnT/w4TjTAn\nT9SMsFbPeZ0qj3oGvTI3dZCA9U3loHaAs04kMT/A/rh2oxSt1zmUh+p0tqvgU8aD0Mvs1PFBh5hM\n2G63cX5+XtXP+ryXgJkgW8Cg4D/eisV4CWdgwEnVFTQsnz/TTuZ2u43VahXn5+dltojBJb8vhgeF\nOxb5I/oOmQq3yxtRM2ePCVVJTwwmICHtZi8HNKmAlLpjptmBsq6g7kncTzCOWdqj4KMHfkYguWoL\nOeAMjDhfoyhM37uyR4CKaYdgMl9FRGUQQB+UBfMZaIRSeXx8jLu7u6Zv+BkoHo6ycpRzs9nE9fV1\n/OQnP4mrq6sSGZuKnDgnpfok2rpxtslPTwewzsgxcnnR3zc3N6Wv1ut1Fa3VE3bV8eySm+e9G+4e\nK28OOrBDxm0a6Q1n5CPqgFKpE3fNrKvKXUqpGb9sAN70v17m5AAGG19cd7Q7fab9qAZZnVDeVzkD\nmXYFiXMqZ9CfmpO76zZ7EMl6Q2c18NElsfjAuby9vY1Xr17Fj370o3j58mXc3t7uNfCj/ntKYmcR\njrCTLe0XABWseHD94n5zP6J+1lG8CoCT0qIyNeeRGZKG54pUFB7ugd75iSTP0r3G6e3nYQcT3wdZ\nyR5dqfYlNYBT6tjZ1FTfioieL9qWg8yocwcnegRHxLxNplv2XHCH52b6HOaYyWq3pSCpbmB+URlj\nTKd6w+ErLU9pcrNPnyrBprhAj2KKlKYTP7FKT1eBQK4gi3Ag9HrPfoEevjYaP06c342ndeLYtk5e\nZuN4ttuh2y0WEfUKL2ACDfIzzo+IaqKJX/Gxr73KOz2MOSpD+5x5EjodDiG/wgZ5+L2WWm7PZiLp\ne1ZRFuw1YwA4lvCnIupXYXHw/1M6mBGfiZMZEeWVB+/evYvT09OIiLi7u4vFYhFnZ2eW+VgAmIG5\nwzVydihIxncPXGsUoiecOepZQc2bUiqWr6ojRRwt6tm3ZmkdA9vURo2cAuHy3DPz/RYcsjJgAMNp\n4BpUipefVRpQhy1f/jNNunYfjgvGHQLP7zXixBEp5R0t++7uruzhUiOAseLDOgB8Matyc3MTr1+/\njlevXsXV1VWs12uipMTiYrE4qhzWDwW575OcoTkksdG9vb0tbV+v1/FzP/dz8e7du7i8vCxG4uTk\npMg43kMFJckn/jpZq5aeG8CCpIEp11b+1t+7ltn7FvxQvY2Ba73MKo/TVyP6WMeNgAj461BW4iAL\n+noOiMx7tRx/qkyUsem8a43HSwNS7rdbGqszlupcfvPNN/HNN9/Emzdv4v7+/r14+30S9ACMO+re\nK1+7+2ib2/PFfcWvcHEOJwAI20rIGPLqSg1th7v+tJSqr959X0V9r3b88F07mC7pHGjlGjc6Zp6F\nZBvZlLmHl1xfMt2u0ta5xbM75zLRb+toltosPR/K/ygDkwHgMeZLrYuDigy8e3hKZym5XJaDn/ZM\nJju3kEteqQVsACzAJ8tqPs7P2EPtGdPQc7j5mSa4GWObhesjG1hh33YZQkUn9AzjV3yzjWOHjCea\nFNurI8aOfa+dl10FrwAAHSRJREFUrh2MifVbn2FeU5uKdsB/gX7Hlr/lchnn5+fVK2wcDRF+NpPb\ngtNjgcGBTXmlJ7Au3smK8vVUWV5hMJqo+dD02TiZcCQj5mUDEfO69pzn6WcGPRGtQCjT9AAlJ72v\nDt2+6MYIsLo0YlYWFnaGGGxvt9vuDGnPcazrinBGZ1ZI41nLkWC4tvWWdmlfKRjVup1i5bHm9muA\ngO8xGOdIj9LEzime45mvnHNxHvH6A5THgBKzKXCybm9vY71ex2azqZbAqXLkiGbV7urnp3M8nwJC\nVPnr8+irq6ur2G63sV6v4+LiIp4/fx5nZ2dxfn4ey+UylstlJfc6ZupIRbQn0eIaeEKNFefBNb7/\nISC6an9EOaXS0RzRzqZrv428QV4+M5L5oBmnfeBYn+ffMJAsb0wdgy/ladUTGpjRAJTqDAA5PMdL\nY7F0SWcucajPq1ev4s2bN/Hq1au4vr6Ou7u7gwzrh/ICJxh60H1/f9/I9mgcOPqs/aO61bUN9zhg\n5max8d0DXDVQk0Z2wZ0CvN1359AaW/aheq7jYI7m+MrvQdvU2atKzLSU1NWtM4TIl/ArqqBxKZkc\nTZ+8ozmVwQ5qC8DnKlo7yf+5nQ4PpZSqg+lUD/MJ4gDFoAMgXLezaHK6jethefhpJcgSg3QO+kAv\nI2B6f39f7cXTctgZZAdC8yOxo6HYBklleB9Orfr+AEc0opZdp3PUAepNUmiQQB0wbRPbbkxC6WQA\n8/6+dvTqYlrUzmJs0PcItCBAjm1Azumv6qHgEMZU24vf+1ZecTCeA45YQcNB2U+9HzPiM3EyMXgP\nDw9xdnZWPOyjo6PqVK4eKGBnzIFFrscZ9ZKHlLxjSAVOzhFVJa708PX5TzSzGKpAkXT9tbZDwUEF\nFuvWRM6jpYK+3ZxYCapjx8CSDQCDSQes9tXJZaqTyH2tziq3j499hoDhm58BzVBi+IYi41k2LJPQ\n9e5wJHHgCO6j//yM7Uwzz+DTrTY5D4L5rfPYhyYHKvQ+kgZQ3r17F6vVKrbb6V2bcDBPTk7i4uIi\nzs7OqmtQnhwVVUdSl867mU8HtCJagxLhHHg2XsCAfUeF+wbtB09x9Fp1SwX2Ywakeg8GjY1rztkA\n3Wx+H8YVHGxJKR20jxDASR1n1YeqGxRAshFkMKdOJmYvb29v4+bmJt6+fRuvX7+ON2/exPX1ddzc\n3BRZ1Fm+fY5m01byC56S2MmEwe+BwOrarjossef922gDA1vVvdoGPIvoO9PHY6IzDz1nm2Wk43NW\n9Vf9OCirl5zzx+Ufkmwua/PnW7PPlbseqGOLhr06QNbRl7ns/TGnqJjT8KnDIM65dDrT1iY84fSY\n2mjoLOgUBua98nq4gWcQdbbzUyeewXSO5mKxKMtkU0rVnmp1whV7cHsjoqw8cPaWbQr+R7Rj41Iv\n4BBR8+Ch+KE8TyaGg/RKNycOtnNSTKYra1CWa+8stwjG1OUofnd16nXm2/Pz8zg5Oan0OSbMVL8y\njzdOJAeHRP9yPpYf7XM8h0AG77WEzcFKF32116E28H3SZ+FkRkQsl8s4Ozsr74/BYPScOufcqMOk\nQjcy5uqEuMTKEs/2hHQovEaBq3PYq18VCveTOnlaXukHU+4hqQK+yTvPEe379tQ4aCRHl3Nw27he\n7gPkY6WjDqZrHxw3JDdLjrrBUzgBDvmxLwpOD6JVCIiABkSzdNkQlFTvQB+Q28xSjYaJx7mc9GbA\n0CdIbPjUCCr/qRzz2CPQxGOKiBuOfOcDgnipFe/vdLMzCpJZ3mplLvflUJNaelLp4p5Mq75g+pyM\nlpI7zgeXFeEPqkqEiB2AdOW4+vmbgzKcR3UiyzjKdkvn1Pnv8Q4/q8tAwS+Ysby6uoqrq6t48+ZN\nvHnzpjiWcOxUn+zTtx87sZO5Xq+f5KeCVp651QAeg2y+pomdT7ckWyPpTL8Dcq2MzDTzd31z2Fqr\ntqpr/Ed4vBuHGzx7qJ4sfdXRrS7+MGIxBfCj+EXB7yaDXmN3d1IHtW3JNMPT1jM+72HkqOp1nqHk\nGUwkDgziOeU98KtbraK6ggNgnzqxHnK6CYeuQJ4Y5GNrCD6jswhUL450uerUniz30ijgMcqv44J7\nKerxdDatwjiUj3WUw/S8TDSi5a+anrpenf11zlwPM+A3sAh8Fz4ZmScleAk5B1Lcyh3GBPBxtK+U\nbv6/XC4j51ydF8J2h51MdjZ/5p3MlFI8f/48Tk9PS0dj4FKalmLg93K5jIh56SgzVs+xUgeOHRWe\nTVKDawWuYfaxk1Yxb6QisT0Q3nvWlauAjJ3P6nkCnRYkdBT4vsT9yn3Pfa2K0T3PwJRBLc/8cJv2\nOewOoDtnB0oAS334PXQ92plHwI9wJiPmzdnsBODlzKpgYBB7ipffozVKLn7eA36fIjkjiI+CCH6G\nAYWOK8bBGYye4t3HG+pIuudq/Cl6Yye/tS6RvqDfHGV3wImXtTDfgRYEN0b6jfsG9wBcmFeLM19a\nb4BpR1aZZ0f6AfXj0AN+lmUZYJB5hlcNgBZc06grAB2Wx8KZxLsub25uqlclMH3a1n1ppIOfmuAk\n3t3dTUtlI8oM9cjZZ33Ep+bqzG5v1Yiu9AAd/A7gHnDfF4SAr9WXO7JBg+R11WF9X9vYOmW9rnQ4\nWvdXODVc7XXM8qU6pcduB9XY4IK2vFpvReM/Ts/kIV5pMcnUIgXZPXvv5Dlitpm6VBbOBcpWh4ix\nAJ5X2pnXP+UL5V1ix4EPHuPr0F8XFxdF5yFoygFoxZ4c5D5kvNhp0lk6daIiohlTvvbUPlA9oeUp\nLlV7xbqLy1SMC2lxmBe85ZLDtsrHuAY71+t39SMiotnbiLbwezIZz3B9zqbiP5xoteOunIgoQfj1\net1gJ8y06ofp/VTps3AyI+bZHTiTaDQcTAbx6ECekeJldJq4s0dC2zpgBY6VKzpbOtHSXuP/s7NX\n1+UcolFShaCGfbFYRN7m6pRKnVV0ZeyjwTm/DfjetU+ViXP4nCBrtMs5dupwojx8u+U0Sr9zhpin\n+J2VmlAHL3lhx1GdpohZ8NUI8szDvGelHk+enav69YkO5Kd0NZ2CdP1eaCG+1SWvOqPNfeki3vzt\n6nG06VLzniGZn8E11+bZZStjtEPdABo688cJkUq9rvzJuqKqK6LiR/znGTt2LtGYnOZdW/uSAgBt\niwMwPH4MMNjZVIPM7VDQhuAPvgHk3r17V/Y06z7DnjwqcBsBqw8BXy6hbVgi7gCLAjB9nk+lfXh4\nKJHriHb2Upfic3l87gHzmAIY1x+1DZFgpuQ1pm/XmOkiAmQTR47GQn90ytUHrF2DO+hLGQ13GRfN\nVGxhS4YjIUcn0PFBvKbtQpEqn3V+h0U8aXsCBakOYrDTA12Ew1Fg3zhPRK2XneOq2IPzAEj/tBLo\nZlvOtOIbYB4TJOqccnkR4yCAwx1IrO/4mtqPcZv2s2AP5zp6GZdhbFn3jWYw8Tx01dFRyxPO7jla\n+b/2R0/XKS9q/pxz2fuPwB+3BdujlstlsVHYp+lWkHDfoL8UX3IbuM8Wi0VZZccObc7zgT/8upKf\n1qE/EZ+Rk4lGw2gyoEdH8HG8PBBsGFUAWPGqE6oDx9918nsce8ZYow9I+wT9UEXAbdRntrl/CI/S\nrsLaZbSepUQ5e06tc0qpJzC959U55Pa53668Xj7mnePj4/L6A32O6cAsOpQgEsaeo6sMmnnmgY2N\nJvDp3shs7f2093OmAyY+TRryDiV1KvWaniTrAkI8Voc4Acr/7HjtczDna3V5nM85BVnGW40VGxLe\nY0011uA9aoCB+jR6zvRECC+y3hSeKM/g3mAslQ7Qi8d5TDBT35P3nuOqKxjUYWRgwbziPvxuMbfH\nhSP+ozQ7UsNswwQgvNlsGuAyci458X5OtwyZAQYDYNX9APoOtPdoUwe0kc1o9eXuR/m/31EZ/Oey\npd5ej1XFJX1qTMcsFomuoX+irXXQNtzKOWJrbBb39WRTo7jdWcvNudAiN6RO7cwpC9PSd0w5j1bf\n1sO6lVcxwJFkPmV9qMCa+dF91H7i2U/9rr9e6tlxnaHdbDZxeXlZPYN+4gPuerqql9hh69lKpZfz\nq41RmUU5uK+Jy+phMbRN939rOUqftpPH3K186ZXdw53umx9lHM8THa5PQRcvmdVv5NVVWqy7mZZR\ne1Au8kPukJe3WvFhc7AZ6mh+yvTZOJk8q8MRLgwKZoM4+s9AJGIeQCi3CMjKzEgMMpEccOXEDOGM\nqHtmH8M4A673laHBTA4Y6dIUlMWffcqlVSKTAVqkFFtRAkojmN6BXVVEDFIBFHtKiq8DMOt1rY/7\ng+/x+5X4Po/9YrGI5XJZDhOJmE/UxD4K8Bf/ZtDGDiiDYwXJEe2Jtkig8akKwDr8kcrm9A9JPQCs\n/cf7JPGNCB7vqeRriO4xb+JZ3nvJS0y5bnYsnEOD8pxxYX2ibSw2l05/i5ijhyqj/297ZxMqyVXF\n8f95M+/1+5jAGCMhxKCJZJOVDkGyCFmqmc3oLiuzENwo6MJFJJtsFXQhiKAYiCJmo2I2gh8IroxG\nyadDTKIBDTFRBJ1Mp3sm866Lrn/1v07d2x/z3uuqMucHTXdX36469+Pcc869t26xD1p2n0POCDfL\nuN0/5ALMklPFMvHOi16jJd8CffIylFDDqW1fl8L5PkjPq0aY6Lbs+h9/b27uxdHja9euZQPSVQKf\n4yKlhEuXLjWccJbN0v9iPgOi92R6pxuSzuNt3KJlbF7unN3MpW0dy5VtobjnSRdFmivi22l9jtXP\n1ewLZrZwnreZjiYGmy0Z84NVKZV1q77myhLyv/Pzq+xNxznVy/1T/WzDXDG5/2TsrLf7ast9IMhz\nchARmAdg/kkBub671OdoG1OnedNBpu9TifZ/1Bvd84H+Eu/PBNrBUG5QjOlWscU5Pc6dr/n/fP78\n50bApQ2peiutSmLZlGTx6bSs/D2KWk75e/63kKialYxqt3Nl4svKL7XVAI+bPeZsCe1NyTfS62h5\nqi75IJV23/f5GifRL6BM3CQu9+iSVfyU46AXQaaZYW9vD7u7uxiNRrVTcPr06XpnLjopLEBWtj7b\nC2hvlqKNUoMz7+S0ncvyMjavQPq7nyn1+WS6RQFSnQ55k7io080tj82Vgx73jrc7c+O3XDnlAkIv\nL6+jS2eWwfTMl19ipufXpRglpfEBsA9AeH7ubqpKzU5Dl8pqfbIDGY/HjdEsXscvDeK7f/5lSsCp\nU828tNrRgvucfDC0ZVv1zqzlgkZ5KoBJFhg1OhDb29v1i7vCjkYjjEaj1ne+uIU9N/ZhXegzpnwg\nqpsm+QBU9ZxpeU7/e8kg5xxv2Kws22WQd7Ipq5aRv07JkC2qK21Ds/bRrh9tb97At3RnyXVopL2O\n+/5A+1DNB7/7gYHcY4FoIFVujtB6R0P7E95Xzd/5qADVM/7GzTi4fIhtZxP3cqWUMB6PG+XGz5qm\nBPOrS/pzjjj7TH8s12/quUvOad4uzM+Rs3n1e/Gf0o+Vfl8hHlwv/FyeepGTrR0l/WqTQez2uaxO\nC7QHU4rpmdYJpuGtlyfH3O4C3PiHwWayZqCZ79/ngbUPGnPX0naXy6v2AdRj6rcOHur1/EvLiufS\nCYpN4gdQacNoA+m/bm9v14/nol3wg6o85u1ZbtDWB1o8lhuIVRukr/kkTHtAtqTv3l8GpPVVDSmh\n+Zi3XJ+T0wH1XzmA71dbHB4eYkv2qdB88BxN2ykedCao9f6v/sZjurSV/iDlpF2jvHrblcYv3O3V\nxyA6EMH4JqXUukbJR+EMuPcTWS60EyyvXEB70ktlgR4FmeoksHB94akzo8suiBacHuM1fHCQmxUs\nXU9lVXwF6aiDny3LOQJ6HaZrdKCuY2f+crOxueuVAli9xiLnQ78vMpC5NDkDUXJWfTpVxJJsJTm8\n85WrWwCt8lJZTp8+jf39/dpw8RzcUIRtVc8xnU7r52Dq717BdXnEohmH2mg37ltqDkK4PzaGplcK\nMI8IjSoDSD7vUl8aVPLY7u5uPajEYJMj3QzSaKDVgOtD5L0x1k5cDV3O6PK49gWKOs+5oGr+v7wO\neQNfmsXT9Lnv2m9oX6UOJNDsW5j33Ky/dxS3bDYSXXJ6m9dqL7HPyUuDq/L4DT/oROiyMh2l1Q00\nmBc/c6dOqwb1OjDlR481Hzry63UxG3CtF9G04Oi3t2/rkFKql/PrbKaWTa5PVdtI/dLy9f2mXk//\nm7M7/pqLgtJZIr61bdvss84eYh7NUS61+flTF6pq+QBRnbI1OVm2fc3/le2bb78NSTLl3pK0ocea\nn3mu/bnn/Vj+3IuCAJ9GdbX0H+q15tO3R233qnc553eZX8Ey5eyMJx88Hw8cBNWBVH6mbdvb22s8\n6oK2jIGH2jYftPr+S+0Y0Lx/lYGF2rlcgOkHO3PvuYAmV6YtP3mrvcTX2y+m19+0nplHtlsvB3sH\nzY/63HC/6fVy+fSfZ7I0n0GqTxcgnIHe3d0FgHpggXIfHs7uwd3b26v7WdYR0Fzm7PtgylFa0aNl\nxw3c9NYcPuWAg6gazOrgRi7+OQl6EWRubW3h7NmzLUUBgN3d3bpwzKxeTksnRGcq9FlCOWXKKR3/\nq85HzilXpeU5gLxRXbXCch16aVSY18kpHsmNYHsZNe+aB910I3dt3xn4oFbzw/+qPLkb3FUePe4N\nin4uGWnvRLGtEB0dKjlhun6dadi+Ll++XAdUeh3tPMys3riKDvR0Oq1nNlV2Opw+yNzassYD7xt5\n5Cg0nfX6eBOD1cYqV/5ZcoPimWPaIbE8aEz39/dxcHCA/f39+sUgk8+8ZDoNQNlB67JIbgLG0V2m\nUQOSu9/Ojw4vMq5elyBl6vWw1GZbxbjMwV4BL2fWoJs1jI46bTQsObm8fDxHboOiup05/fTG279r\ngEkDSl3RkWENPvl/HZzhd+ZH+yc1ohy11eV3ZlY7dVeuXGnNNmgfpcuHcn13tk5L+tL42jygQVop\nX7we85Dr565evVovg2J9qBOqL30YuDoZOzs79XE6Kd7x8AM4/vdFLy23nL6U2uMi57Z0bBmr6qov\n85KMuQCRx70/oL95G6DpvFPtba2Xw+tcSb5S3kuBprfzPgBQ3WTfQTurG9Xp0kW2Mf0fBz/5ne2U\nbXTZEnfCvoZLyDcFdYi2jYOr+plPSNB0AOpjLBf6rX7Fji+TRUFjabCV6f2AKsue3xWvr4v0RP3m\nEupD6kaIQPu2Ms2X9omaP72u5klnEn1dqew81kpb2f9cueh1Dg/nTxPQdNRhradTp07V/TUw3zFe\n+wLqFc/hV+yo3GwrtCX0I3WCbmtr9nxW2juW+87ODq5cudKYad/e3q6fHX1S9CLIvHr1Ki5evFhn\nWiuK69XZKHSjDJ3NZIVqBSu+g/INlN91maN3snKKVzJEpNSZq+LpsUVGl99zCpIzOqr8arB8PtTJ\n8cs8S46kllXOIKlcOSPmlcdTMnx6PKcYKh8NImX1efJ1oKOw/J1tgzMHly9frgNyLtGZTqe180el\np9GbTCZ1J8NNP3iuyWSSzX8rkJYkacHyKIX55/lKM6aN6xf8t5wRoVHc3d2tA8qDg4P6xWNqhDnK\ne+bMmcZxNbTqLHN5EZ0Svyw25xTnDLCvS34vOcFe/30ZzH+f10pDF11Ret3zBpJ15Mt32UxXzrnV\ntq7Gz+TxSbNbsppOLY2mXnOZk6/XywWkuoSI6CwejaT2PdQrPab/0f5JgykaTe3TGVzS4HonDpjf\nz8Vgt7SzdL4CVktGfD/m+83S7E31pf74zjvvYDweYzwe48yZM/XsKPPugxk6FtPpFKPRqB7AmUwm\ntdPHclZdYjkuCixzOrWKzczpU8n2LWqPuXS530rHlp0799ui9pE7n9bnMmcup9M87s+5cjtdInvJ\nfvu26FflEA7Osi+gndONSKhztI3axlJKte937do1jMdjvP3225hOp/XunW+99RYmk0m9Uojtn/Z0\nMplkZVsWAF0vnDHSwVQdZD04OGjMatL+mVn9mcEKy4Z9GPVNZ518kEkZVE9zt0j44FLLxR/37U3T\n8vfqQOsxPdpOcvqiPrvqP21Acun1urk+Rfs6zb9OEMzOlY8F/PmBys0S/9rLQXum1/crsDQP3o/Q\nY/TJcr6oHwTwsmgsRJ2kHGovqU/8PBqNMJ1OG7cz0a86yQEaOwkFXFsIs38CuAzgX13LcgRuwrDl\nB4afh6HLD5xsHj6QUnofEDrXI4aeh6HLD4TOrUPUd/cMXX4gdG4dor77wdDzsBGd8/QiyAQAM3sq\npXR313JcL0OXHxh+HoYuP7DZPAy9vIYuPzD8PAxdfiB0bh2GLj8w/DwMXX4gdG4dhi4/EHnoA13J\nf3I7ggRBEARBEARBEATvOiLIDIIgCIIgCIIgCI6NPgWZ3+5agCMydPmB4edh6PIDm83D0Mtr6PID\nw8/D0OUHQufWYejyA8PPw9DlB0Ln1mHo8gORhz7Qify9uSczCIIgCIIgCIIgGD59mskMgiAIgiAI\ngiAIBk7nQaaZfcLMXjSzl83soa7lWRUze9XMnjOzp83sqerYjWb2CzN7qXp/T9dyKmb2qJm9aWbP\ny7GszDbjG1W9PGtm57qTvJY1J/8jZvZaVQ9Pm9l5+e3LlfwvmtnHu5F6jpndZma/NrM/mdkLZvaF\n6vhG6yB0bnOEznVL6NzRCJ3bPKFzoXOhc5sldO4E6yD3gOhNvQCcAvAKgDsA7AB4BsBdXcq0huyv\nArjJHfsqgIeqzw8B+ErXcjr57gNwDsDzy2QGcB7AzzB75Pg9AJ7sqfyPAPhSJu1dVXsaAbi9amen\nOpb/FgDnqs83APhzJefG6iB0rhdtNnRuc/KHzh1N9tC5fsgfOreeDKFzm5U5dK5b+TvXudKr65nM\njwJ4OaX0l5TSFQCPA7jQsUxH4QKAx6rPjwH4ZIeytEgp/QbAv93hkswXAHwvzfgtgLNmdstmJM1T\nkL/EBQCPp5SmKaW/AngZs/bWGSml11NKf6w+XwJwEcCt2GwdhM5tkNC50DmEzm2U0LnQOYTObZTQ\nudC5El0HmbcC+Jt8/3t1bAgkAD83sz+Y2WerYzenlF6vPv8DwM3diLYWJZmHVDefr6b8H5VlJL2W\n38w+COAjAJ7EZuug1+WyhNC5/hA6tzq9LpclhM71h9C51el1uSwhdK4/hM4dka6DzCFzb0rpHID7\nAXzOzO7TH9NsTnpQW/cOUWYA3wLwIQAfBvA6gK91K85yzOwMgB8B+GJK6b/620DrYFOEzvWD0Ll3\nD6Fz/SB07t1D6Fw/CJ07BroOMl8DcJt8f391rPeklF6r3t8E8BPMpsvf4JRz9f5mdxKuTEnmQdRN\nSumNlNK1lNIhgO9gvmyhl/Kb2TZmncAPUko/rg5vsg56WS6rEDrXD0Ln1qaX5bIKoXP9IHRubXpZ\nLqsQOtcPQueOh66DzN8DuNPMbjezHQAPAHiiY5mWYmYHZnYDPwP4GIDnMZP9wSrZgwB+2o2Ea1GS\n+QkAn652oboHwH9k2r03uHXkn8KsHoCZ/A+Y2cjMbgdwJ4DfbVo+xcwMwHcBXEwpfV1+2mQdhM51\nT+jchgidu35C5/pD6NzahM51T+jchuiJzuVJ3e/qdB6znZBeAfBw1/KsKPMdmO0u9QyAFyg3gPcC\n+BWAlwD8EsCNXcvq5P4hZtP+VzFbg/2ZksyY7Tr1zapengNwd0/l/34l37OYKc4tkv7hSv4XAdzf\nA/nvxWy5wrMAnq5e5zddB6FznbfZ0LnNyR86d/0yh871R/7QufXlCJ3bnNyhc93K3wudy72sumAQ\nBEEQBEEQBEEQHJmul8sGQRAEQRAEQRAE/0dEkBkEQRAEQRAEQRAcGxFkBkEQBEEQBEEQBMdGBJlB\nEARBEARBEATBsRFBZhAEQRAEQRAEQXBsRJAZBEEQBEEQBEEQHBsRZAZBEARBEARBEATHRgSZQRAE\nQRAEQRAEwbHxP1ikDH/N0bHYAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1152x288 with 8 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fF4eXc5m3q8W",
        "colab_type": "text"
      },
      "source": [
        "# ResNet50\n",
        "Try the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mMMEmjMf3ssW",
        "colab_type": "code",
        "outputId": "03fbf9d9-5fad-491f-eb04-a5bc2bcb6dc0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 250,
          "referenced_widgets": [
            "6cbe84c6aa4a4f34ab24bf0b97269ada",
            "6c650fe9843c40958da8d3da0ab7c543",
            "3f428a46df034a1485fffc8adc37425b",
            "a33b6772a9604b0ea75b359a830609de",
            "c78c009e46864818b07aee30ab5d4ee7",
            "972a61021325415388ce58cf16f56aeb",
            "720654d38cb14b38bc25b2e7f7da9031",
            "ea24b57aae1c4877870a56e8e027abe0"
          ]
        }
      },
      "source": [
        "resnet = models.resnet50(pretrained=True)\n",
        "inputs, labels = next(iter(train_dl))\n",
        "inputs, labels = Variable(inputs), Variable(labels)\n",
        "print(inputs.size())\n",
        "outputs = resnet(inputs)\n",
        "outputs.size()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/resnet50-19c8e357.pth\" to /root/.cache/torch/checkpoints/resnet50-19c8e357.pth\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6cbe84c6aa4a4f34ab24bf0b97269ada",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, max=102502400), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "torch.Size([4, 3, 224, 224])\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([4, 1000])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "phlx6HxW86E0",
        "colab_type": "text"
      },
      "source": [
        "构建模型并训练"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ixi0eunB9EZy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def train_model(dataloders, model, criterion, optimizer, scheduler, num_epochs=2):\n",
        "    since = time.time()\n",
        "    best_model_wts = model.state_dict()\n",
        "    best_acc = 0.0\n",
        "    dataset_sizes = {'train': len(dataloders['train'].dataset), \n",
        "                     'valid': len(dataloders['valid'].dataset)}\n",
        "    print(dataset_sizes)\n",
        "    for epoch in range(num_epochs):\n",
        "        for phase in ['train', 'valid']:\n",
        "\n",
        "            if phase == 'train':\n",
        "                scheduler.step()\n",
        "                model.train(True)\n",
        "            else:\n",
        "                model.train(False)\n",
        "\n",
        "            running_loss = 0.0\n",
        "            running_corrects = 0\n",
        "            for inputs, labels in dataloders[phase]:\n",
        "                inputs, labels = Variable(inputs), Variable(labels)\n",
        "\n",
        "                optimizer.zero_grad()\n",
        "\n",
        "                outputs = model(inputs)\n",
        "                _, preds = torch.max(outputs.data, 1)\n",
        "                loss = criterion(outputs, labels)\n",
        "\n",
        "                if phase == 'train':\n",
        "                    loss.backward()\n",
        "                    optimizer.step()\n",
        "                #print(\"fgfs\")\n",
        "                running_loss += loss.item() * inputs.size(0)\n",
        "                running_corrects += torch.sum(preds == labels.data)\n",
        "            \n",
        "            if phase == 'train':\n",
        "                train_epoch_loss = running_loss / dataset_sizes[phase]\n",
        "                train_epoch_acc = running_corrects.double() / dataset_sizes[phase]\n",
        "            else:\n",
        "                valid_epoch_loss = running_loss / dataset_sizes[phase]\n",
        "                valid_epoch_acc = running_corrects.double() / dataset_sizes[phase]\n",
        "                dataset_sizes_phase = dataset_sizes[phase]\n",
        "\n",
        "            if phase == 'valid' and valid_epoch_acc > best_acc:\n",
        "                best_acc = valid_epoch_acc\n",
        "                best_model_wts = model.state_dict()\n",
        "\n",
        "        print('Epoch [{}/{}] train loss: {} acc: {} ' \n",
        "              'valid loss: {:.4f} acc: {:.4f}'.format(\n",
        "                epoch, num_epochs - 1,\n",
        "                train_epoch_loss, train_epoch_acc, \n",
        "                valid_epoch_loss, valid_epoch_acc))\n",
        "            \n",
        "    print('Best val Acc: {:4f}'.format(best_acc))\n",
        "\n",
        "    model.load_state_dict(best_model_wts)\n",
        "    return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zGC-fouA_A0o",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "resnet = models.resnet50(pretrained=True)\n",
        "# freeze all model parameters\n",
        "for param in resnet.parameters():\n",
        "    param.requires_grad = False\n",
        "#第一层卷积接受通道改为3\n",
        "resnet.conv1 = torch.nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)\n",
        "#提取fc层中固定的参数\n",
        "num_ftrs = resnet.fc.in_features\n",
        "#修改类别为4\n",
        "resnet.fc = torch.nn.Linear(num_ftrs, 4)\n",
        "#model.last_linear = nn.Linear(465696, config.num_classes, bias=True)\n",
        "criterion = torch.nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.SGD(resnet.fc.parameters(), lr=0.001, momentum=0.9)\n",
        "exp_lr_scheduler = lr_scheduler.StepLR(optimizer, step_size=7, gamma=0.1)\n",
        "\n",
        "dloaders = {'train':train_dl, 'valid':valid_dl}\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wtXyoD7cZszl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#for inputs, labels in dloaders['train']:\n",
        "                #inputs, labels = Variable(inputs), Variable(labels)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "obu1Edbo_GbB",
        "colab_type": "code",
        "outputId": "44219981-a749-41e3-caa6-9a088ae93115",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "start_time = time.time()\n",
        "start_time"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1584930621.8559957"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7VLWL4h3_PH5",
        "colab_type": "code",
        "outputId": "c6651f7b-3c35-4e23-804e-f667a68e3d94",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model = train_model(dloaders, resnet, criterion, optimizer, exp_lr_scheduler, num_epochs=32)\n",
        "print('Training time: {:10f} minutes'.format((time.time()-start_time)/60))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'train': 11602, 'valid': 2900}\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/optim/lr_scheduler.py:122: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n",
            "  \"https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\", UserWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch [0/31] train loss: 1.4936934551932117 acc: 0.35873125323220134 valid loss: 1.2823 acc: 0.4414\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch [1/31] train loss: 1.47997533132487 acc: 0.3715738665747285 valid loss: 1.4071 acc: 0.4097\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch [2/31] train loss: 1.4596065512208605 acc: 0.38226167902085845 valid loss: 1.4280 acc: 0.3845\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch [3/31] train loss: 1.4793714577936752 acc: 0.3794173418376142 valid loss: 1.4810 acc: 0.3907\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
            "  \n"
          ],
          "name": "stderr"
        }
      ]
    }
  ]
}